# Quasi-free-standing AA-stacked bilayer graphene induced by calcium
intercalation of the graphene-silicon carbide interface

Antonija Grubišić-Čabo<EMAIL_ADDRESS>School of Physics and
Astronomy, Monash University, Clayton, VIC 3800, Australia  Zernike Institute
for Advanced Materials, University of Groningen, 9747 AG Groningen, The
Netherlands  Jimmy C. Kotsakidis  School of Physics and Astronomy, Monash
University, Clayton, VIC 3800, Australia  Laboratory for Physical Sciences,
University of Maryland, College Park, MD, 20740, USA  Yuefeng Yin  Department
of Materials Science and Engineering, Monash University, Clayton, VIC 3800,
Australia  ARC Centre of Excellence in Future Low Energy Electronics
Technologies (FLEET), Monash University, Clayton, VIC 3800, Australia  Anton
Tadich  ARC Centre of Excellence in Future Low Energy Electronics Technologies
(FLEET), Monash University, Clayton, VIC 3800, Australia  Australian
Synchrotron, Clayton, VIC 3168, Australia  Department of Physics, La Trobe
University, Bundoora, VIC 3086, Australia  Matthew Haldon  School of Physics
and Astronomy, Monash University, Clayton, VIC 3800, Australia  Sean Solari
School of Physics and Astronomy, Monash University, Clayton, VIC 3800,
Australia  John Riley  Department of Physics, La Trobe University, Bundoora,
VIC 3086, Australia  Eric Huwald  Department of Physics, La Trobe University,
Bundoora, VIC 3086, Australia  Kevin M. Daniels  Department of ECE, University
of Maryland, College Park, MD 20742, USA  Rachael L. Myers-Ward  U.S. Naval
Research Laboratory, Washington, D.C., 20375, USA  Mark T. Edmonds  School of
Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia  ARC
Centre of Excellence in Future Low Energy Electronics Technologies (FLEET),
Monash University, Clayton, VIC 3800, Australia  Nikhil Medhekar  Department
of Materials Science and Engineering, Monash University, Clayton, VIC 3800,
Australia  ARC Centre of Excellence in Future Low Energy Electronics
Technologies (FLEET), Monash University, Clayton, VIC 3800, Australia  D. Kurt
Gaskill  Institute for Research in Electronics and Applied Physics, University
of Maryland, College Park, MD 20742, USA  Michael S. Fuhrer
<EMAIL_ADDRESS>School of Physics and Astronomy, Monash University,
Clayton, VIC 3800, Australia  ARC Centre of Excellence in Future Low Energy
Electronics Technologies (FLEET), Monash University, Clayton, VIC 3800,
Australia

###### Abstract

We study quasi-freestanding bilayer graphene on silicon carbide intercalated
by calcium. The intercalation, and subsequent changes to the system, were
investigated by low-energy electron diffraction, angle-resolved photoemission
spectroscopy (ARPES) and density-functional theory (DFT). Calcium is found to
intercalate only at the graphene-SiC interface, completely displacing the
hydrogen terminating SiC. As a consequence, the system becomes highly n-doped.
Comparison to DFT calculations shows that the band dispersion, as determined
by ARPES, deviates from the band structure expected for Bernal-stacked bilayer
graphene. Instead, the electronic structure closely matches AA-stacked bilayer
graphene on Ca-terminated SiC, indicating a spontaneous transition from AB- to
AA-stacked bilayer graphene following calcium intercalation of the underlying
graphene-SiC interface.

Keywords: Graphene, Calcium, Intercalation, Electronic structure, ARPES, DFT

##  1 Introduction

Graphene, a single layer of graphite [1] is notable for its unique
bandstructure with massless Dirac Fermions [2], which give rise to a plethora
of exotic physical phenomena, such as a $\pi$-Berry phase [3, 4, 5], Klein
tunnelling [6] and an unusual quantum Hall effect [3].

In contrast, the most typical form of bilayer graphene, so called AB- or
Bernal stacked bilayer graphene (Supplementary Information Figure S3A, B), has
a completely different electronic structure with massive, yet gapless, Dirac
fermions, and a Berry phase of 2$\pi$ [7, 8, 9, 10]. In principle, other types
of stacking, such as AA-stacking (Supplementary Information Figure S3C, D),
exist. AA-stacking is a metastable stacking, where graphene layers lie
directly above one another. Consequently, AA-stacked graphene has an
electronic structure which can be considered as a superposition of two single-
layer spectra, preserving massless Dirac fermions and a $\pi$-Berry phase [11,
12]. Despite many interesting properties predicted for AA-stacked bilayer
graphene, including a recent prediction that it might host a fractional metal
state [13], there are very few experimental realisations [14, 11, 15, 16, 17,
18]. Out of the few reported cases, the majority have been found in lithium
intercalated systems [15, 16], or contained within very small regions
otherwise surrounded by AB-stacked graphene [18, 17].

One of the most promising methods for graphene production in terms of
scalability is the growth of graphene on silicon carbide (SiC) which allows
formation of large-scale graphene with high carrier mobility [19, 20, 21, 22].
Graphene on SiC can either be epitaxial, i.e. directly grown on the SiC, with
a buffer layer in between the graphene and the SiC interface, or quasi-
freestanding graphene–most commonly created via hydrogen intercalation of
epitaxial graphene [23, 24], in which graphene retains the properties expected
for the isolated layer [25]. Hydrogen is not the only element that can be used
to create quasi-freestanding graphene on SiC by means of intercalation [26];
various other elements can be used, such as gold [27, 28], iron [29, 30],
oxygen [31], lithium [32, 15, 16, 33], magnesium [34, 35, 36], calcium [34,
37, 38, 39, 40, 41, 40], antimony [42] and ytterbium [43]. The majority of the
intercalation studies have been done on epitaxial monolayer and bilayer
graphene on SiC, with very few intercalation studies on already quasi-
freestanding, hydrogen intercalated, graphene [44, 45, 34]. Of particular
interest to us is calcium intercalated graphene, whose study was inspired by
the bulk superconducting graphite intercalation compound CaC6 [46, 38, 47,
48]. The majority of calcium intercalation experiments have been performed on
graphene grown on SiC, as this allows for growth of large-area graphene that
can be characterised with various surface characterisation techniques, such as
X-ray photoelectron spectroscopy, angle-resolved photoemission spectroscopy
(ARPES), low-energy electron diffraction (LEED) and scanning tunnelling
microscopy [34, 7, 49, 50]. Calcium intercalation is known to strongly n-type
dope graphene, an effect which has been extensively studied [7, 50], however,
the impact of calcium intercalation on the structural aspects of graphene and
the precise positioning of calcium atoms remained somewhat ambiguous [34, 41,
49]. Recent research using X-ray photoelectron spectroscopy (XPS) by
Kotsakidis et al. [34] has shed light on this, revealing that calcium is
situated at the interface between the SiC substrate and graphene buffer layer,
with work by Toyama et al. [40] further confirming that calcium prefers to go
to the SiC interface.

In this paper, we report calcium intercalation of quasi-freestanding bilayer
graphene (QFSBLG) on SiC. Using a combination of LEED, ARPES and density-
functional theory (DFT), calcium is found to intercalate only at the interface
between graphene and SiC, fully replacing hydrogen in the structure, and not
between the graphene layers. This results in highly n-doped, quasi-
freestanding bilayer graphene (Ca-QFSBLG) with a drastically altered
electronic structure, as seen by ARPES. Comparison with DFT shows the
structure to be in close agreement with AA-stacked bilayer graphene,
indicating a spontaneous transition from AB- to AA-stacking, which has not
been previously observed for calcium intercalated graphene.

##  2 Materials and Methods

### Sample preparation

QFSBLG samples on SiC were grown on semi-insulating 6H-SiC(0001) substrate as
described in Ref. [23]. Sample preparation, ARPES and LEED measurements were
carried out at the Toroidal Analyzer endstation at the Soft X-ray Beamline of
the Australian Synchrotron. Samples were introduced to ultra-high vacuum (UHV,
base pressure of 1 $\times$ 10-10 mbar), and annealed over night at 773 – 823
K. Sample cleanliness was confirmed by LEED and ARPES. A calcium effusion cell
was baked at 423 K overnight and outgassed at 588 K. Once the pressure reached
1 × 10-8 mbar, the effusion cell was inserted into the UHV preparation
chamber. Calcium (dendritic pieces, 99.99%, Sigma-Aldrich) was intercalated
under graphene following modified recipe from Ref. [34]: Calcium was
evaporated for 15 min, with the calcium cell held at 688 K, and deposited on
the graphene/SiC substrate held at room temperature. The thickness of
deposited calcium layer was 22 $\mathring{A}$, as determined by a quartz
crystal microbalance. Following the deposition, the graphene/SiC substrate was
annealed at 773 K for 15 minutes, in order to facilitate calcium
intercalation.

### Angle-resolved photoemission spectroscopy & Low-energy electron
diffraction

Structural characterisation of samples was undertaken using a LEED (OCITM 3
grid reverse view optics, 200 $\mu$m spot size) at room temperature, in the
endstation used for ARPES. ARPES measurements used a toroidal-type angle-
resolving endstation [51] at the Soft X-Ray Beamline of the Australian
Synchrotron. All ARPES data was taken at room temperature with photon energy
(h$\nu$) of 100 eV using linearly polarised light at normal incidence to the
sample. The beam spot size was 100 $\mu$m × 60 $\mu$m. The binding energy
(EBin) scale for all spectra is referenced to the Fermi energy (EF),
determined using the Fermi edge of a gold foil reference sample in electrical
contact with the sample. The toroidal analyser permits all polar ($\Theta$)
emission angles (-90∘ to +90∘) to be measured along a high-symmetry azimuth
($\phi$) of the surface containing the $\bar{\Gamma}$ point. This unique
geometry allows for measurement of the Dirac cone along the
$\bar{K}-\bar{\Gamma}-\bar{K}$ high-symmetry direction without the need for
complex alignment of the spectrometer. Under this geometry, the polarisation
vector of the X-rays is entirely contained in the detection plane. The
estimated momentum and energy resolution are $\approx$ 0.02
$\mathring{A}^{-1}$ and $\approx$ 150 meV.

### Density-functional theory & Tight-binding

First principles density-functional theory calculations were implemented using
the Vienna ab initio Simulation Package (VASP) to calculate the electronic
structure of Ca-QFSBLG [52]. The Perdew-Burke-Ernzehof (PBE) form of the
generalized gradient approximation (GGA) was used to describe electron
exchange and correlation [53]. A semi-empirical functional (DFT-D2) was
employed to describe van der Waals interactions in the system [54]. The
kinetic energy cut-off for the plane-wave basis set was set to 500 eV. We used
a 9 $\times$ 9 $\times$ 1 $\Gamma$-centred k-point mesh for sampling the
Brillouin zone. The unfolded band structure and Fermi surface were obtained
using the KPROJ program based on the k-projection method [55, 56]. Tight-
binding calculations were performed in Igor Pro Wavemetrics software based on
Ref. [12] for AA-stacked bilayer graphene, and Refs. [8, 9] for AB-stacked
bilayer graphene. Tight-binding calculations are presented along the
$\bar{K}-\bar{\Gamma}-\bar{K}$ high-symmetry direction. Parameters used for
tight binding calculations were t = 3.05 ($\pm$ 0.05) and $\gamma_{1}$ = 0.4
for AB-stacked QFSBLG, and t = 2.9 ($\pm$ 0.05) and $\gamma_{1}$ = 0.4 for AA-
stacked Ca-QFSBLG.

##  3 Results and discussion

###  3.1 Experimental results

QFSBLG samples, Figure 1A, prepared as described in Ref.[34] were loaded into
the UHV chamber and annealed to remove surface adsorbates, as described in
Methods. Following the annealing procedure, LEED data was taken on the clean
sample, as shown in Figure 1C. LEED data shows typical diffraction pattern of
quasi-freestanding graphene, with only (1 $\times$ 1) graphene spots (red
circles) and (1 $\times$ 1) SiC spots rotated 30∘ with respect to graphene
(purple circles) visible. Following calcium intercalation (Figure 1B), the SiC
(1 $\times$ 1) spots are less intense compared to the clean QFSBLG, but no
other significant changes can be seen in LEED, as shown in Figure 1D. After
the second intercalation step, (Figure 1E), drastic changes can be observed in
the LEED pattern: (1 $\times$ 1) SiC spots are almost completely gone, while
(1 $\times$ 1) graphene spots are much weaker and broader. Blurring of the
graphene (1 $\times$ 1) spots suggests additional scattering, likely from
calcium atoms accumulating on the surface of the sample in a disordered
manner. A new feature can also be observed in the diffraction pattern in
Figure 1E, a diffuse ring, marked by a yellow arrow, with a radius
corresponding to that of a ($\sqrt{3}\times\sqrt{3})$R30∘ calcium structure.
Ring like features observed in LEED usually point towards rotationally
disordered system [57], suggesting calcium is not ordered either under
graphene or on its surface. This is in contrast to previous LEED data on
calcium intercalated graphene on SiC, where a sharp single domain
($\sqrt{3}\times\sqrt{3})$R30∘ LEED pattern coming from calcium intercalation
is observed [49, 34, 40]. One possible explanation for this discrepancy is
that in our system, disorder comes from calcium that did not intercalate, but
is instead deposited on the surface of the sample where it does not order.
Another possibility is that while calcium is replacing hydrogen at the SiC
interface, it does so without rotational order, though the reason for this
difference in structure is not clear.

Figure 1: Quasi freestanding bilayer graphene (QFSBLG) before and after
calcium intercalation. A sketch of QFSBLG before and after intercalation,
where hydrogen is replaced by calcium at the SiC interface is shown in (A) and
(B), respectively. (C) LEED image of QFSBLG prior calcium intercalation, (D)
following first, and (E) following second calcium intercalation. Red circles
indicate the (1 $\times$ 1) graphene spots, purple circles indicate the (1
$\times$ 1) SiC spots. Yellow arrow points to the ring arising from disordered
Ca. All data taken at an incident beam energy of 126 eV and room temperature.
Figure 2: ARPES measurements of clean and calcium intercalated quasi-
freestanding bilayer graphene (Ca-QFSBLG) after the first intercalation step.
(A) Fermi surface and (B) band structure around the $\bar{K}$ point of QFSBLG,
and (C) Fermi surface and (D) band structure around the $\bar{K}$ point of Ca-
QFSBLG. In (B) and (D) both raw ARPES spectra (left) and a 2D curvature of
ARPES spectra (right) are shown. Following calcium intercalation, a
significant change in the electronic structure and doping level can be seen.
(E) and (F) show tight-binding calculations for AB- (left) and AA-stacked
(right) bilayer graphene before and after calcium intercalation, respectively.
Red arrows in (D) and (F) indicate location of band crossing between the top
and bottom layer band, and a lack of the band gap for AA-stacked bilayer
graphene. Blue arrows in (D) and (F) show the Dirac point in the top graphene
layer which appears ungapped.

Following structural characterization by LEED, we proceed with the electronic
structure investigation using ARPES [58] which allows direct imaging of the
electronic bands. Due to an increase in observed disorder for the second
intercalation step in LEED, Figure 1E, we will only focus on the first
intercalation step for the ARPES investigation. Figure 2 shows intercalation
induced changes in the electronic structure of graphene, as observed by ARPES.
Changes are tracked in the energy–momentum cuts taken at the Fermi surface
(Figure 2A,C) and along the $\bar{K}-\bar{\Gamma}-\bar{K}$ high-symmetry
direction (Figure 2B,D). In order to enhance the dispersive features of
graphene around the $\bar{K}$ point, we use a two-dimensional (2D) curvature
analysis technique [59], shown in Figure 2B and 2D, on the right. The Fermi
surface map Figure 2A, shows the first Brillouin zone of pristine QFSBLG prior
calcium intercalation, with six hole pockets visible at the Brillouin zone
boundary. As expected for the case of bilayer graphene [7], two sets of bands
are visible in the energy dispersion data, Figure 2B. Since the samples are
p-doped, the Dirac point is located above the Fermi level (0.2 eV, see
Supplementary Information), which can be clearly seen in the energy dispersion
and the 2D curvature data, Figure 2B.

Upon calcium intercalation, profound changes can be observed in the electronic
structure: two sets of electron pockets can be seen at the Fermi surface
(Figure 2C), and system exhibits high levels of n-doping (Figure 2D). In order
to better understand the dispersions and changes arising from calcium
intercalation, we compare our experimental data to simple tight-binding models
for AA- and AB-stacked graphene (Figs. 2E,F) [12, 8]. The same model was used
to estimate Dirac point position, doping, Fermi wavevector and Fermi velocity.
We used the band position data obtained from momentum dispersion curves
(MDCs), to refine the tight-binding model, and select the appropriate graphene
stacking. More information about this can be found in the Supplementary
Information. Prior to calcium intercalation, the system is found to be
p-doped, with the Dirac point located at DP = (0.20 $\pm$ 0.02) eV above the
Fermi level, and a Fermi wavevector ($k_{F}$) value of $k_{F}$ = (0.057 $\pm$
0.007) Å-1, corresponding to a hole carrier density of n${}_{h}^{total}$ =
(5.17 $\pm$ 0.08)$\times 10^{12}$ cm-2, in agreement with literature values
for hydrogen intercalated graphene on SiC [24, 25]. Following calcium
intercalation (Figure 2C,D) the sample is transformed into highly n-doped
system in which no clear band gap can be seen. This structure is in stark
contrast to the C6CaC6 electronic structure, where the ordered calcium phase
gives rise to a folding of the $\pi$ bands of graphene, resulting in the
folded bands appearing close to the $\bar{\Gamma}$ point [49, 40, 60]. In the
case of Ca-QFSBLG, no states are observed at the $\bar{\Gamma}$ point,
supporting the interpretation that in our case calcium is not ordered in
C6CaC6 structure, in agreement with the LEED data (Figure 1D,E). Absence of
the signatures of C6CaC6 structure implies that calcium is not intercalated
between the layers, in agreement with recent work by Kotsakidis et al. [34].
Rather, we instead observe a quasi-freestanding bilayer graphene that is
n-doped. Nevertheless, there are several discrepancies between our results
(Figure 2D) and what is expected from simply n-doped (quasi-freestanding)
bilayer graphene [7]. The most obvious discrepancy is a lack of band gap in
the system (see red and blue arrow in the Figure 2D), which is expected for
the simple case of AB-stacked bilayer graphene [7, 35]. This is reminiscent of
the structure expected for AA-stacked bilayer graphene where a band gap is not
expected, and the electronic bands still have massless character as for the
case of monolayer graphene [12]. Taking this into the account, we modelled
both AA- and AB-stacked bilayer graphene with tight-binding (Figure 2E,F) and
DFT (Figure 3). From the tight-binding model, the best agreement was obtained
for AA-stacked bilayer graphene, where the inner band Fermi wavevector was
$k_{F}^{inner}$ = (0.086 $\pm$ 0.007) Å-1, and the outer band was
$k_{F}^{outer}$ = (0.280 $\pm$ 0.007 )Å-1. These values correspond to a total
electron density of ne = n${}_{e}^{inner}$+n${}_{e}^{outer}$ = (1.37 $\pm$
0.06) $\times$ 1014 cm-2, a two orders of magnitude increase in carrier
concentration with respect to the pristine QFSBLG. The aforementioned dramatic
increase in the carrier concentration is accompanied with small reduction in
Fermi velocity of graphene– Ca-QFSBLG has Fermi velocity $v_{F}$ = (0.93 $\pm$
0.02) $\times 10^{6}$ m/s, while QFSBLG has Fermi velocity $v_{F}$ = (0.99
$\pm$ 0.02) $\times 10^{6}$ m/s, suggesting that no significant
renormalisaiton of Fermi velocity due to transformation of stacking [61],
change of the dielectric constant of the graphene’s surroundings [62], or
many-body interactions [63] takes place. Lastly, while the observed increase
in the carrier density is significant, it is lower than values observed for
the case when calcium goes both to the SiC interface and in between the sheets
of bilayer graphene [40], thus underpinning the notion of intercalation
occurring solely at the interface of our system.

###  3.2 Theoretical results

We further examine the nature of stacking in Ca-QFSBLG by performing DFT
calculations of the electronic structure of AB- and AA-stacked Ca-QFSBLG. We
first calculate the calcium intercalation energy based on the Eq.1 as follows:

$E_{I}=E(SiC/graphene)+E(Ca)-E(SiC/graphene+Ca),$ (1)

where $E_{I}$ is the intercalation energy, $E(SiC/graphene)$, $E(Ca)$ and
$E(SiC/graphene+Ca)$ are the energy of SiC/graphene heterostructure (SiC
covalently bonded with graphene plus a monolayer graphene), atomic energy of
calcium in its bulk state and the energy of SiC/graphene system upon calcium
intercalation, respectively. Once calcium is placed below bilayer graphene, a
small difference is found in the formation energy between the AA- and AB-
stacked bilayer graphene, as shown in Table 1, with AB-stacking being slightly
favourable. This small difference in formation energy suggests it is plausible
that AA-stacking could indeed be a stable phase in Ca-QFSBLG, similar to what
has been observed for the case of lithium intercalation [44, 15]. As both
structures appear energetically stable, and are close in the formation energy,
DFT band structure calculations were performed for AA- and AB-stacking in
order to determine which structure fits experimental ARPES data better.

Stacking type | DFT-D2 energy [eV] | DFT-D3 energy [eV]
---|---|---
AA | 0.94 | 1.08
AB | 0.98 | 1.09
Table 1: Calculation for Intercalation energy, AA vs. AB, using different van
der Waals corrections. Figure 3: DFT calculations for calcium intercalated
QFSBLG. The unfolded band structure of calcium intercalated bilayer graphene
for the case of AB- and AA-stacking is shown in (A) and (B), respectively. The
contribution from the top graphene layer is shown in blue, and from the bottom
layer in red. Arrows indicate a band gap for the case of AB-stacking, (A), and
a band crossing for the case of AA-stacking in Ca-QFSBLG, (B).

The unfolded band structure of AA- and AB-stacked Ca-QFSBLG is shown in Figure
3. For the case of AB-stacked Ca-QFSBLG (Figure 3A), a large band gap,
approximately 0.38 eV in size, is found between the top of the valence band
and bottom of a conduction band, situated 0.51 eV below the Fermi level. This
structure is similar to the one observed for magnesium intercalated graphene
on SiC [35], where a band gap of 0.35 eV was observed. In the case of AA-
stacked bilayer graphene (Figure 3B), the structure is markedly different, and
no band gap is found between the top of the valence band and bottom of a
conduction band. Instead, a smaller 0.2 eV gap is identified 1.05 eV below the
Fermi level located only in the bottom graphene layer. In contrast, the top
graphene layer is gapless, and nearly indistinguishable from the pristine
monolayer graphene. The latter structure is in good agreement with the
experimental data shown in Figure 2, particularly in the region where the top-
and bottom-layer derived bands cross at finite momentum (red arrows in Figs.
2D and 2F) and the Dirac point of the top graphene layer (blue arrows in Figs.
2D and 2F), in agreement with our experimental ARPES results which show Ca-
QFSBLG as AA-stacked.

##  4 Conclusion

Calcium intercalation was successfully achieved in quasi-freestanding bilayer
graphene on hydrogenated SiC, resulting in significant changes to the system.
Upon calcium intercalation, calcium replaced hydrogen at the SiC interface,
leading to a switch from p-type doping to n-type doping. This transition was
accompanied with almost two orders of magnitude change in the carrier
concentration, going from n${}_{h}^{total}$ = 5.17 $\times 10^{12}$ cm-2 to
n${}_{e}^{total}$ = 1.37 $\times$ 1014 cm-2, while retaining the quasi-
freestanding nature, and exhibiting minimal change of the Fermi velocity.
Structurally, the intercalation process resulted in a transformation from AB-
stacked to AA-stacked bilayer graphene, a shift facilitated by a small
difference in the formation energy between the two stacking types. As a
result, the electronic band structure is significantly altered. The top layer
of graphene is nearly indistinguishable from a pristine monolayer, retaining
the ungapped Dirac point. While previous reports show indirect evidence of
inhomogeneous AA- and AB-stacked regions in epitaxial graphene following
lithium intercalation and de-intercalation, to our knowledge, this is the
first report of the preparation of large-area, uniform quasi-freestanding AA-
stacked graphene following calcium intercalation. This opens the door to
further study of the properties of this distinct new graphene system with its
unique band structure.

## Acknowledgement

This work was supported by the Australian Research Council under awards
DP150103837, DP200101345 and FL120100038. This research was undertaken on the
Soft X-ray spectroscopy beamline at the Australian Synchrotron, part of ANSTO.
J.C.K. gratefully acknowledges support from the Australian Government Research
Training Program, and the Monash Centre for Atomically Thin Materials. Y.Y.
and N.M. gratefully acknowledge the support from the Australian Research
Council (CE17010039) and the computational support from the National Computing
Infrastructure and Pawsey Supercomputing Facilities. D.K.G., R.L.M-W., and
K.M.D. acknowledge support by core programs at the U.S. Naval Research
Laboratory funded by the Office of Naval Research.

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# VGGFace2: A dataset for recognising faces across pose and age

Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi and Andrew Zisserman Visual
Geometry Group, Department of Engineering Science, University of Oxford
<EMAIL_ADDRESS>
###### Abstract

In this paper, we introduce a new large-scale face dataset named VGGFace2. The
dataset contains $3.31$ million images of $9131$ subjects, with an average of
$362.6$ images for each subject. Images are downloaded from Google Image
Search and have large variations in pose, age, illumination, ethnicity and
profession (e.g. actors, athletes, politicians).

The dataset was collected with three goals in mind: (i) to have both a large
number of identities and also a large number of images for each identity; (ii)
to cover a large range of pose, age and ethnicity; and (iii) to minimise the
label noise. We describe how the dataset was collected, in particular the
automated and manual filtering stages to ensure a high accuracy for the images
of each identity.

To assess face recognition performance using the new dataset, we train
ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional
Neural Networks on VGGFace2, on MS-Celeb-1M, and on their union, and show that
training on VGGFace2 leads to improved recognition performance over pose and
age. Finally, using the models trained on these datasets, we demonstrate
state-of-the-art performance on the face recognition of IJB datasets,
exceeding the previous state-of-the-art by a large margin. The dataset and
models are publicly
available111http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.

###### Index Terms:

face dataset; face recognition; convolutional neural networks

††publicationid: pubid: 978-1-5386-2335-0/18/$31.00 ©2018 IEEE

##  I Introduction

Concurrent with the rapid development of deep Convolutional Neural Networks
(CNNs), there has been much recent effort in collecting large scale datasets
to feed these data-hungry models. In general, recent datasets (see Table I)
have explored the importance of intra- and inter-class variations. The former
focuses on depth (many images of one subject) and the latter on breadth (many
subjects with limited images per subject). However, none of these datasets was
specifically designed to explore pose and age variation. We address that here
by designing a dataset generation pipeline to explicitly collect images with a
wide range of pose, age, illumination and ethnicity variations of human faces.

We make the following _four_ contributions: first, we have collected a new
large scale dataset, VGGFace2, for public release. It includes over nine
thousand identities with between 80 and 800 images for each identity, and more
than 3M images in total; second, a dataset generation pipeline is proposed
that encourages pose and age diversity for each subject, and also involves
multiple stages of automatic and manual filtering in order to minimise label
noise; third, we provide template annotation for the test set to explicitly
explore pose and age recognition performance; and, finally, we show that
training deep CNNs on the new dataset substantially exceeds the state-of-the-
art performance on the IJB benchmark datasets [13, 23, 14]. In particular, we
experiment with the recent Squeeze and Excitation network [9], and also
investigate the benefits of first pre-training on a dataset with breadth (MS-
Celeb-1M [7]) and then fine tuning on VGGFace2.

The rest of the paper is organised as follows: We review previous dataset in
Section II, and give a summary of existing public dataset in Table I. Section
III gives an overview of the new dataset, and describes the template
annotation for recognition over pose and age. Section IV describes the dataset
collection process. Section V reports state-of-the-art performance of several
different architectures on the IJB-A [13], IJB-B [23] and IJB-C [14]
benchmarks.

##  II Dataset Review

Datasets | # of subjects | # of images | # of images per subject | manual identity labelling | pose | age | year
---|---|---|---|---|---|---|---
LFW [10] | $5,749$ | $13,233$ |  $1$/$2.3$/$530$ | - | - | - | 2007
YTF [24] | $1,595$ |  $3,425$ videos | - | - | - | - | 2011
CelebFaces+ [21] | $10,177$ | $202,599$ | $19.9$ | - | - | - | 2014
CASIA-WebFace [26] | $10,575$ | $494,414$ |  $2$/$46.8$/$804$ | - | - | - | 2014
IJB-A [13] | $500$ |  $5,712$ images, $2,085$ videos | $11.4$ | - | - | - | 2015
IJB-B [23] | $1,845$ |  $11,754$ images, $7,011$ videos | $36.2$ | - | - | - | 2017
IJB-C [14] | $3,531$ |  $31,334$ images, $11,779$ videos | $36.3$ | - | - | - | 2018
VGGFace [17] | $2,622$ |  $2.6$ M |  $1,000$/$1,000$/$1,000$ | - | - | Yes | 2015
MegaFace [12] | $690,572$ |  $4.7$ M |  $3$/$7$/$2469$ | - | - | - | 2016
MS-Celeb-1M [7] | $100,000$ |  $10$ M | $100$ | - | - | - | 2016
UMDFaces [5] | $8,501$ | $367,920$ | $43.3$ | Yes | Yes | Yes | 2016
UMDFaces-Videos [4] | $3,107$ |  $22,075$ videos | - | - | - | - | 2017
VGGFace2 (this paper) | $9,131$ |  $3.31$ M |  $80$/$362.6$/$843$ | Yes | Yes | Yes | 2018

TABLE I: Statistics for recent public face datasets. The three entries in the
‘per subject’ column are the minimum/average/maximum per subject.

In this section we briefly review the principal “in the wild” datasets that
have appeared recently. In 2007, the Labelled Faces in the Wild (LFW) dataset
[10] was released, containing $5,749$ identities with $13,000$ images.

The CelebFaces+ dataset [21] was released in 2014, with $202,599$ images of
$10,177$ celebrities. The CASIA-WebFace dataset [26] released the same year
that has $494,414$ images of $10,575$ people. The VGGFace dataset [17]
released in 2015 has $2.6$ million images covering $2,622$ people, making it
amongst the largest publicly available datasets. The curated version, where
label noise is removed by human annotators, has $800,000$ images with
approximately $305$ images per identity. Both the CASIA-WebFace and VGGFace
datasets were released for training purposes only.

MegaFace dataset [12] was released in 2016 to evaluate face recognition
methods with up to a million distractors in the gallery image set. It contains
$4.7$ million images of $672,057$ identities as the training set. However, an
average of only $7$ images per identity makes it restricted in its per
identity face variation. In order to study the effect of pose and age
variations in recognising faces, the MegaFace challenge [12] uses the subsets
of FaceScrub [15] containing $4,000$ images from $80$ identities and FG-NET
[16] containing $975$ images from $82$ identities for evaluation.

Microsoft released the large Ms-Celeb-1M dataset [7] in 2016 with $10$ million
images from $100$k celebrities for training and testing. This is a very useful
dataset, and we employ it for pre-training in this paper. However, it has two
limitations: (i) while it has the largest number of training images, the
intra-identity variation is somewhat restricted due to an average of $81$
images per person; (ii) images in the training set were directly retrieved
from a search engine without manual filtering, and consequently there is label
noise. The IARPA Janus Benchmark-A (IJB-A) [13], Benchmark-B (IJB-B) [23] and
Benchmark-C (IJB-C) [14] datasets were released as evaluation benchmarks (only
test) for face detection, recognition and clustering in images and videos.

Unlike the above datasets which are geared towards image-based face
recognition, the Youtube Face (YTF) [24] and UMDFaces-Videos [4] datasets aim
to recognise faces in unconstrained videos. YTF contains $1,595$ identities
and $3,425$ videos, whilst UMDFaces-Videos is larger with $3,107$ identities
and $22,075$ videos (the identities are a subset of those in UMDFaces [5]).

Apart form these public datasets, Facebook and Google have large in-house
datasets. For instance, Facebook [22] trained a face identification model
using $500$ million images of over $10$ million subjects. The face recognition
model by Google [19] was trained using $200$ million images of $8$ million
identities.

##  III An Overview of The VGGFace2

###  III-A Dataset Statistics

The VGGFace2 dataset contains $3.31$ million images from $9131$ celebrities
spanning a wide range of ethnicities, e.g. it includes more Chinese and Indian
faces than VGGFace (though, the ethnic balance is still limited by the
distribution of celebrities and public figures), and professions (e.g.
politicians and athletes). The Images were downloaded from Google Image Search
and show large variations in pose, age, lighting and background. The dataset
is approximately gender-balanced, with $59.3$% males, varying between $80$ and
$843$ images for each identity, with $362.6$ images on average. It includes
human verified bounding boxes around faces, and five fiducial keypoints
predicted by the model of [27]. In addition, pose (yaw, pitch and roll) and
apparent age information are estimated by our pre-trained pose and age
classifiers (Pose, age statistics and example images are shown in Figure 1).

(a) pose statistics (b) age statistics

(c) John Wesley Shipp (d) Leymah Gbowee

(e) Princess Haya Bint Al Hussein

(f) Julio César Chávez Jr.

(g) Roy Jones Jr. (h) Ruby Lin

(i) Additi Gupta (j) Lee Joon-gi

Figure 1: (a-b) VGGFace2 poses and ages statistics. (c-j) example images for
eight subjects with different ethnicities.

The dataset is divided into two splits: one for training having $8631$
classes, and one for evaluation (test) with $500$ classes.

Stage | Aim | Type | # of subject | total # of images | Annotation effort
---|---|---|---|---|---
1 | Name list selection | M |  $500$K | 50.00 million |  $3$ months
2 | Image downloading | A | $9244$ |  $12.94$ million | -
3 | Face detection | A | $9244$ |  $7.31$ million | -
4 | Automatic filtering by classification | A | $9244$ |  $6.99$ million | -
5 | Near duplicate removal | A | $9244$ |  $5.45$ million | -
6 | Final automatic and manual filtering | A/M | $9131$ |  $3.31$ million |  $21$days
TABLE II: Dataset statistics after each stage of processing in the collection
pipeline.

Figure 2: VGGFace2 template examples. Left: pose templates from three
different viewpoints (arranged by row) – frontal, three-quarter, profile.
Right: age templates for two subjects for young and mature ages (arranged by
row).

###  III-B Pose and Age Annotations

The VGGFace2 provides annotation to enable evaluation on two scenarios: face
matching across different poses, and face matching across different ages.
Pose templates. A template here consists of five faces from the same subject
with a consistent pose. This pose can be frontal, three-quarter or profile
view. For a subset of 300 subjects of the evaluation set, two templates ($5$
images per template) are provided for each pose view. Consequently there are
$1.8$K templates with $9$K images in total. An examples is shown in Figure 2
(left).
Age templates. A template here consists of five faces from the same subject
with either an apparent age below 34 (deemed young), or 34 or above (deemed
mature). These are provided for a subset of $100$ subjects from the evaluation
set with two templates for each age period, therefore, there are $400$
templates with a total of $2$K images. Examples are show in Figure 2 (right).

##  IV DATASET COLLECTION

In this section, we describe the dataset collection process, including: how a
list of candidate identities was obtained; how candidate images were
collected; and, how the dataset was cleaned up both automatically and
manually. The process is summarised in Table II.

###  IV-A Stage 1: Obtaining and selecting a name list

We use a similar strategy to that proposed by [17]. The first stage is to find
as many subjects as possible that have a sufficiently distinct set of images
available, for example, celebrities and public figures (e.g. actors,
politicians and athletes). An initial list of $500$k public figures is
obtained from the Freebase knowledge graph [2].

An annotator team is then used to remove identities from the candidate list
that do not have sufficient distinct images. To this end, for each of the
$500$K names, $100$ images are downloaded using Google Image Search and human
annotators are instructed to retain subjects for which approximately $90$% or
more of the $100$ images belong to a single identity. This removes candidates
who do not have sufficient images or for which Google Image Search returns a
mix of people for a single name. In this manner, we reduce the candidates to
only $9244$ names. Attribute information such as ethnicity and kinship is
obtained from DBPedia [1].

###  IV-B Stage 2: Obtaining images for each identity

We query in Google Image Search and download $1000$ images for each subject.
To obtain images with large pose and age variations, we then append the
keyword ‘sideview’ and ‘very young’ to each name and download $200$ images for
each. This results in $1400$ images for each identity.

###  IV-C Stage 3: Face detection

Faces are detected using the model provided by [27]. We use the hyper-
parameters recommended in that work to favor a good trade-off between
precision and recall. The face bounding box is then extended by a factor of
$0.3$ to include the whole head. Moreover, five facial landmarks are predicted
by the same model.

###  IV-D Stage 4: Automatic filtering by classification

The aim of this stage is to remove outlier faces for each identity
automatically. This is achieved by learning a classifier to identify the
faces, and removing possible erroneous faces below a classification score. To
this end, 1-vs-rest classifiers are trained to discriminate between the $9244$
subjects. Specifically, faces from the top $100$ retrieved images of each
identity are used as positives, and the top $100$ of all other identities are
used as negative for training. The face descriptor features are obtained from
the VGGFace [17] model. Then, the scores (between $0$ and $1$) from the
trained model is used to sort images for each subject from most likely to
least likely. By manually checking through images from a random $500$
subjects, we choose a threshold of $0.5$ and remove any faces below this.

###  IV-E Stage 5: Near duplicate removal

The downloaded images also contain exact or near duplicates due to the same
images being found at different internet locations, or images differing only
slightly in colour balance or JPEG artifacts for example. To alleviate this,
duplicate images are removed by clustering VLAD descriptors for all images
remaining at _stage $4$_ and only retaining one image per cluster [3, 11].

###  IV-F Stage 6: Final automatic and manual filtering

At this point, two types of error may still remain: first, some classes still
have outliers (i.e. images that do not belong to the person); and second, some
classes contain a mixture of faces of more than one person, or they overlap
with another class in the dataset. This stage addresses these two types of
errors with a mix of manual and automated algorithms.

Detecting overlapped subjects. Subjects may overlap with other subjects. For
instance, ‘Will I Am’ and ‘William James Adams’ in the candidate list refer to
the same person. To detect confusions for each class, we randomly split the
data for each class in half: half for training and the other for testing.
Then, we train a ResNet-50 [8] and generate a confusion matrix by calculating
top-1 error on the test samples. In this manner, we find $20$ subjects
confused with others. In this stage, we removed $19$ noisy classes. In
addition, we remove $94$ subjects with samples less than $80$ images, which
results in a final list of $9131$ identities.

Removing outlier images for a subject. The aim of this filtering, which is
partly manual, is to achieve a purity greater than $96$%. We found that for
some subjects, images with very high classifier scores at _stage $4$_ can also
be noisy. This happens when the downloaded images contain couples or band
members who always appear together in public. In this case, the classifiers
trained with these mixed examples at _stage $4$_ tend to fail.

We retrain the model based on the current dataset, and for each identity the
classifier score is used to divide the images into $3$ sets: H (i.e. high
score range [$1$, $0.95$]), I (i.e. intermediate score range ($0.95$, $0.8$])
and L (i.e. low score range ($0.8$, $0.5$]). Human annotators clean up the
images for each subject based on their scores, and the actions they carry out
depends on whether the set H is noisy or not. If the set (H) contains several
different people (noise) in a single identity folder, then set I and L (which
have lower confidence scores), will undoubtedly be noisy as well, so all three
sets are cleaned manually. In contrast, if set H is clean, then only set L
(the lowest scores which is supposed to be the most noisy set) is cleaned up.
After this, a new model is trained on the cleaned set H and L, and set I
(intermediate scores, noise level is also intermediate) is then cleaned by
model prediction. This procedure achieves very low label noise without
requiring manual checking of every image.

###  IV-G Pose and age annotations

We train two networks to obtain the pose and age information for the dataset.
To obtain head pose (roll, pitch, yaw), a $5$-way classification ResNet-$50$
[8] is trained on the CASIA-WebFace dataset [26]. Then, this trained model is
used to predict pose for all the images in the dataset.

Similarly, to estimate the apparent age, a $8$-way classification ResNet-$50$
[8] is trained on IMDB-WIKI - $500$k$+$ dataset [18]. Ages of faces are then
predicted by this model.

##  V EXPERIMENTS

In this section, we evaluate the quality of the VGGFace2 dataset by conducting
a number of baseline experiments. We report the results on the VGGFace2 test
set, and evaluate on the public benchmarks IJB datasets[13, 23, 14]. The
subjects in our training dataset are disjoint with the ones in benchmark
datasets. We also remove the overlap between MS-Celeb-1M and the two
benchmarks when training the networks.

###  V-A Experimental setup

Architecture. ResNet-50 [8] and SE-ResNet-50 [9] (SENet for short) are used as
the backbone architectures for the comparison amongst training datasets. The
Squeeze-and-Excitation (SE) blocks [9] adaptively recalibrate channel-wise
feature responses by explicitly modelling channel relationships. They can be
integrated with modern architectures, such as ResNet, and improve its
representational power. This has been demonstrated for object and scene
classification, with a Squeeze-and-Excitation network winning the ILSVRC 2017
classification competition.

The following experiments are developed under four settings: (a) networks are
learned from scratch on VGGFace [17] (VF for short); (b) networks are learned
from scratch on MS-Celeb-1M (MS1M for short) [7]; (c) networks are learned
from scratch on VGGFace2 (VF2 for short); and, (d) networks are first pre-
trained on MS1M, and then fine-tuned on VGGFace2 (VF2_ft for short).

Similarity computation. In all the experiments (i.e. for both verification and
identification), we need to compute the similarity between subject templates.
A template is represented by a single vector computed by aggregating the face
descriptors of each face in the template set. In section V-B, the template
vector is obtained by averaging the face descriptors of the images and SVM
classifiers are used for identification. In sections V-C and V-D for IJB-A and
IJB-B, where the template may contain both still images and video frames, we
first compute the media vector (i.e. from images or video frames) by averaging
the face descriptors in that media. A template vector is then generated by
averaging the media vectors in that template, which is then L$2$ normalised.
Cosine similarity is used to represent the similarity between two templates.

A face descriptor is obtained from the trained networks as follows: first the
extended bounding box of the face is resized so that the shorter side is $256$
pixels; then the centre $224\times 224$ crop of the face image is used as
input to the network. The face descriptor is extracted from from the layer
adjacent to the classifier layer. This leads to a $2048$ dimensional
descriptor, which is then L$2$ normalised.

Training implementation details. All the networks are trained for
classification using the soft-max loss function. During training, the extended
bounding box of the face is resized so that the shorter side is $256$ pixels,
then a $224\times 224$ pixels region is randomly cropped from each sample. The
mean value of each channel is subtracted for each pixel.

Monochrome augmentation is used with a probability of $20\%$ to reduce the
over-fitting on colour images. Stochastic gradient descent is used with mini-
batches of size $256$, with a balancing-sampling strategy for each mini-batch
due to the unbalanced training distributions. The initial learning rate is
$0.1$ for the models trained from scratch, and this is decreased twice with a
factor of $10$ when errors plateau. The weights of the models are initialised
as described in [8]. The learning rate for model fine-tuning starts from
$0.005$ and decreases to $0.001$.

###  V-B Experiments on the new dataset

In this section, we evaluate ResNet-50 trained from scratch on the three
datasets as described in the Sec. V-A, and VGGFace2 test set. We test
identification performance and also similarity over pose and age, and validate
the capability of VGGFace2 to tackle pose and age variations.

Face identification. This scenario aims to predict, for a given test image,
whose face it is. Specifically, for each of the $500$ subjects in the
evaluation set, $50$ images are randomly chosen as the testing split and the
remaining images are used as the training split. This training split is used
to learn 1-vs-rest SVM classifiers for each subject. A top-1 classification
error is then used to evaluate the performance of these classifiers on the
test images. As shown in Table III, there is a significant improvement for the
model trained on VGGFace2 rather than on VGGFace. This demonstrates the
benefit of increasing data variation (e.g, subject number, pose and age
variations) in the VGGFace2 training dataset. More importantly, models trained
on VGGFace2 also achieve better result than that on MS1M even though it has
tenfold more subjects and threefold more images, demonstrating the good
quality of VGGFace2. In particular, the very low top-1 error of VGGFace2
provides evidence that there is very little label noise in the dataset – which
is one of our design goals.

Training dataset | VGGFace | MS1M | VGGFace2
---|---|---|---
Top-1 error (%) | $10.6$ | $5.6$ | $3.9$
TABLE III: Identification performance (top-1 classification error) on the
VGGFace2 test set for ResNet models trained on different datasets. A lower
value is better.

Training dataset | VGGFace | MS1M | VGGFace2
---|---|---|---
| front | three-quarter | profile | front | three-quarter | profile | front | three-quarter | profile
front | $0.5781$ | $0.5679$ | $0.4821$ | $0.5661$ | $0.5582$ | $0.4715$ | $0.6876$ | $0.6821$ | $0.6222$
three-quarter | $0.5706$ | $0.5957$ | $0.5345$ | $0.5628$ | $0.5766$ | $0.5036$ | $0.6859$ | $0.6980$ | $0.6481$
profile | $0.4859$ | $0.5379$ | $0.5682$ | $0.4776$ | $0.5064$ | $0.5094$ | $0.6264$ | $0.6515$ | $0.6488$

TABLE IV: Face probing across poses. Similarity scores are evaluated across
pose templates. A higher value is better.

Figure 3: Histograms of similarity scores for front-to-profile matching for
the models trained on different datasets.

Figure 4: Two example templates of front-to-profile matching. Left: the
similarity scores produced by VGGFace, MS1M, VGGFace2 are $0.41$, $0.35$ and
$0.59$, respectively; Right: the scores are $0.41$, $0.31$ and $0.57$,
respectively.

Probing across pose. This test aims to assess how well templates match across
three pose views: front, three-quarter and profile views. As described in
section III-B, $300$ subjects in the evaluation set are annotated with pose
templates, and there are six templates for each subject: two each for front,
three-quarter view and profile views.

These six templates are divided into two sets, one pose for each set, and a
$3\times 3$ similarity matrix is constructed between the two sets. Figure 4
visualises two example of these cosine similarity scores for front-to-profile
templates.

Table IV compares the similarity matrix averaged over the $300$ subjects. We
can observe that (i) all the three models perform better when matching similar
poses, i.e., front-to-front, three-quarter-to-three-quarter and profile-to-
profile; and (ii) the performance drops when probing for different poses,
e.g., front-to-three-quarter and front-to-profile, showing that recognition
across poses is a much harder problem. Figure 3 shows histograms of similarity
scores. It is evident that the mass of the VGGFace2 trained model is to the
right of the MS1M and VGGFace trained models. This clearly demonstrates the
benefit of training on a dataset with larger pose variation.

Training dataset | VGGFace | MS1M | VGGFace2
---|---|---|---
| young | mature | young | mature | young | mature
young | $0.5231$ | $0.4338$ | $0.4983$ | $0.4005$ | $0.6256$ | $0.5524$
mature | $0.4394$ | $0.5518$ | $0.4099$ | $0.5276$ | $0.5607$ | $0.6637$

TABLE V: Face probing across ages. Similarity scores are evaluated across age
templates. A higher value is better.

Figure 5: Histograms of similarity scores for young-to-mature matching for the
models trained on different datasets.

Figure 6: Two example templates of young-to-mature matching. Left: the
similarity scores produced by VGGFace, MS1M, VGGFace2 are $0.42$, $0.30$ and
$0.58$, respectively; Right: the scores are $0.43$, $0.41$ and $0.73$,
respectively.

Probing across age. This test aims to assess how well templates match across
age, for two ages ranges: young and mature ages. As described in section
III-B, $100$ subjects in the evaluation set are annotated with age templates,
and there are four templates for each subject: two each for young and mature
faces.

For each subject a $2\times 2$ similarity matrix is computed, where an element
is the cosine similarity between two templates. Figure 6 shows two examples of
the young-to-mature templates, and their similarity scores.

Table V compares the similarity matrix averaged over the $100$ subjects as the
model changes. For all the three models, there is always a big drop in
performance when matching across young and mature faces, which reveals that
young-to-mature matching is substantially more challenging than young-to-young
and mature-to-mature. Moreover, young-to-young matching is more difficult than
mature-to-mature matching. Figure 5 illustrates the histograms of the young-
to-mature template similarity scores.

Discussion. In the evaluation of pose and age protocols, models trained on
VGGFace2 always achieve the highest similarity scores, and MS1M dataset the
lowest. This can be explained by the fact that the MS1M dataset is designed to
focus more on inter-class diversities, and this harms the matching performance
across different pose and age, illustrating the value of VGGFace2 in having
more intra-class diversities that cover large variations in pose and age.

Figure 7 shows the top $3$ and bottom $3$ front-to-profile template matches
sorted using the similarity scores produced by the ResNet model trained on
VGGFace2. We can observe that the model gives high scores to front-to-profile
templates where there is little variation beyond pose; while it gives lower
scores to templates where many other variations exist, such as expression and
resolution.

Figure 8 shows the top $3$ and bottom $3$ young-to-mature template matches
sorted using the similarity scores produced by the ResNet model trained on
VGGFace2. It can be seen that the model gives low scores to young-to-mature
templates where variations such as pose and occlusion exist.

Figure 7: Top $3$ and bottom $3$ front-to-profile template matches sorted
using the similarity score produced by a ResNet model trained on VGGFace2. The
front template is on the left, and the profile template on the right in each
row.

Figure 8: Top $3$ and bottom $3$ young-to-mature template matches sorted using
the similarity score produced by a ResNet model trained on VGGFace2. The young
template is on the left, and the mature template on the right in each row.

###  V-C Experiments on IJB-A

Training dataset | Arch. | 1:1 Verification TAR | 1:N Identification TPIR
---|---|---|---
|  | FAR=$0.001$ | FAR=$0.01$ | FAR=$0.1$ | FPIR=$0.01$ | FPIR=$0.1$ | Rank-$1$ | Rank-$5$ | Rank-$10$
VGGFace [17] | ResNet-50 | $0.620\pm 0.043$ | $0.834\pm 0.021$ | $0.954\pm 0.005$ | $0.454\pm 0.058$ | $0.748\pm 0.024$ | $0.925\pm 0.008$ | $0.972\pm 0.005$ | $0.983\pm 0.003$
MS$1$M [7] | ResNet-50 | $0.851\pm 0.030$ | $0.939\pm 0.013$ | $0.980\pm 0.003$ | $0.807\pm 0.041$ | $0.920\pm 0.012$ | $0.961\pm 0.006$ | $0.982\pm 0.004$ | $0.990\pm 0.002$
VGGFace2 | ResNet-50 | $0.895\pm 0.019$ | $0.950\pm 0.005$ | $0.980\pm 0.003$ | $0.844\pm 0.035$ | $0.924\pm 0.006$ | $0.976\pm 0.004$ | $0.992\pm 0.002$ | $0.995\pm 0.001$
VGGFace2_ft | ResNet-50 | $0.908\pm 0.017$ | $0.957\pm 0.007$ | $0.986\pm 0.002$ | $0.861\pm 0.027$ | $0.936\pm 0.007$ | $0.978\pm 0.005$ | $0.992\pm 0.003$ | $0.995\pm 0.001$
VGGFace2 | SENet | $0.904\pm 0.020$ | $0.958\pm 0.004$ | $0.985\pm 0.002$ | $0.847\pm 0.051$ | $0.930\pm 0.007$ | $0.981\pm 0.003$ | $\mathbf{0.994\pm 0.002}$ | $\mathbf{0.996\pm 0.001}$
VGGFace2_ft | SENet | $\mathbf{0.921\pm 0.014}$ | $\mathbf{0.968\pm 0.006}$ | $\mathbf{0.990\pm 0.002}$ | $\mathbf{0.883\pm 0.038}$ | $\mathbf{0.946\pm 0.004}$ | $\mathbf{0.982\pm 0.004}$ | $0.993\pm 0.002$ | $0.994\pm 0.001$
Crosswhite et al. [6] | - | $0.836\pm 0.027$ | $0.939\pm 0.013$ | $0.979\pm 0.004$ | $0.774\pm 0.049$ | $0.882\pm 0.016$ | $0.928\pm 0.010$ | $0.977\pm 0.004$ | $0.986\pm 0.003$
Sohn et al. [20] | - | $0.649\pm 0.022$ | $0.864\pm 0.007$ | $0.970\pm 0.001$ | - | - | $0.895\pm 0.003$ | $0.957\pm 0.002$ | $0.968\pm 0.002$
Bansalit et al. [4] | - | $0.730^{\dagger}$ | $0.874$ | $0.960^{\dagger}$ | - | - | - | - | -
Yang et al. [25] | - | $0.881\pm 0.011$ | $0.941\pm 0.008$ | $0.978\pm 0.003$ | $0.817\pm 0.041$ | $0.917\pm 0.009$ | $0.958\pm 0.005$ | $0.980\pm 0.005$ | $0.986\pm 0.003$

TABLE VI: Performance evaluation on the IJB-A dataset. A higher value is
better. The values with ${\dagger}$ are read from [4].

Figure 9: Results on the IJB-A dataset (average over 10 splits). Left: ROC
(higher is better); Middle: DET (lower is better); Right: CMC (higher is
better).

In this section, we compare the performance of the models trained on the
different datasets on the public IARPA Janus Benchmark A (IJB-A dataset) [13].

The IJB-A dataset contains $5712$ images and $2085$ videos from $500$
subjects, with an average of 11.4 images and 4.2 videos per subject. All
images and videos are captured from unconstrained environment and show large
variations in expression and image qualities. As a pre-processing, we detect
the faces using MTCNN [27] to keep the cropping consistent between training
and evaluation.

IJB-A provides ten-split evaluations with two standard protocols, namely, 1:1
face verification and 1:N face identification, where we directly extract the
features from the models for the test sets and use cosine similarity score.
For verification, the performance is reported using the true accept rates
(TAR) vs. false positive rates (FAR) (i.e. receiver operating characteristics
(ROC) curve). For identification, the performance is reported using the true
positive identification rate (TPIR) vs. false positive identification rate
(FPIR) (equivalent to a decision error trade-off (DET) curve) and the Rank-N
(i.e. the cumulative match characteristic (CMC) curve). Table VI and Figure 9
presents the comparison results.

The effect of training set. We first investigate the effect of different
training sets based on the same architecture ResNet-50 (Table VI), and start
with networks trained from scratch. we can observe that the model trained on
VGGFace2 outperforms the one trained on VGGFace by a large margin, even though
VGGFace has a similar scale (2.6M images) it has fewer identities and pose/age
variations (and more label noise). Moreover, the model of VGGFace2 is
significantly superior to the one of MS1M which has $10$ times subjects over
our dataset. Specially, it achieve $\sim 4.4\%$ improvement over MS1M on
FAR=$0.001$ for verification, $\sim 3.7\%$ on FPIR=$0.01$ and $\sim 1.5\%$ on
Rank-$1$ for identification.

When comparing with the results of existing works, the model trained on
VGGFace2 surpasses previously reported results on all metrics (best to our
knowledge, reported on IJB-A 1:1 verification and 1:N identification
protocols), which further demonstrate the advantage of the VGGFace2 dataset.
In addition, the generalisation power can be further improved by first
training with MS1M and then fine-tuning with VGGFace2 (i.e. “VGGFace2_ft”),
however, the difference is only $0.908$ vs. $0.895$.

Many existing datasets are constructed by following the assumption of the
superiority of wider dataset (more identities) [26, 7, 12], where the huge
number of subjects would increase the difficulty of model training. In
contrast, VGGFace2 takes both aspects of breath (subject number) and depth
(sample number per subject) into account, guaranteeing rich intra-variation
and inter-diversity.

The effect of architectures. We next investigate the effect of architectures
trained on VGGFace2 (Table VI). The comparison between ResNet-50 and SENet
both learned from scratch reveals that SENet has a consistently superior
performance on both verification and identification. More importantly, SENet
trained from scratch achieves comparable results to the fine-turned ResNet-50
(i.e. first pre-trained on the MS1M dataset), demonstrating that the diversity
of our dataset can be further exploited by an advanced network. In addition,
the performance of SENet can be further improved by training on the two
datasets VGGFace2 and MS1M, exploiting the different advantages that each
offer.

###  V-D Experiments on IJB-B

Training dataset | Arch. | 1:1 Verification TAR | 1:N Identification TPIR
---|---|---|---
|  | FAR=$1E-5$ | FAR=$1E-4$ | FAR=$1E-3$ | FAR=$1E-2$ | FPIR=$0.01$ | FPIR=$0.1$ | Rank-$1$ | Rank-$5$ | Rank-$10$
VGGFace [17] | ResNet-50 | $0.342$ | $0.535$ | $0.711$ | $0.850$ | $0.429\pm 0.024$ | $0.635\pm 0.015$ | $0.752\pm 0.038$ | $0.843\pm 0.032$ | $0.874\pm 0.026$
MS$1$M [7] | ResNet-50 | $0.548$ | $0.743$ | $0.857$ | $0.935$ | $0.662\pm 0.036$ | $0.810\pm 0.028$ | $0.865\pm 0.053$ | $0.917\pm 0.032$ | $0.936\pm 0.024$
VGGFace2 | ResNet-50 | $0.647$ | $0.784$ | $0.878$ | $0.938$ | $0.701\pm 0.038$ | $0.824\pm 0.034$ | $0.886\pm 0.032$ | $0.936\pm 0.019$ | $0.953\pm 0.013$
VGGFace2_ft | ResNet-50 | $0.671$ | $0.804$ | $0.891$ | $0.947$ | $0.702\pm 0.041$ | $0.843\pm 0.032$ | $0.894\pm 0.039$ | $0.940\pm 0.022$ | $0.954\pm 0.016$
VGGFace2 | SENet | $0.671$ | $0.800$ | $0.888$ | $0.949$ | $0.706\pm 0.047$ | $0.839\pm 0.035$ | $0.901\pm 0.030$ | $0.945\pm 0.016$ | $0.958\pm 0.010$
VGGFace2_ft | SENet | $\mathbf{0.705}$ | $\mathbf{0.831}$ | $\mathbf{0.908}$ | $\mathbf{0.956}$ | $\mathbf{0.743\pm 0.037}$ | $\mathbf{0.863\pm 0.032}$ | $\mathbf{0.902\pm 0.036}$ | $\mathbf{0.946\pm 0.022}$ | $\mathbf{0.959\pm 0.015}$
Whitelam et al. [23] | - | $0.350$ | $0.540$ | $0.700$ | $0.840$ | $0.420$ | $0.640$ | $0.790$ | $0.850$ | $0.900$

TABLE VII:  Performance evaluation on the IJB-B dataset. A higher value is
better. The results of [23] are read from the curves reported in the paper.
Note, [23] has a different evaluation for the verification protocol where
pairs generated from different galleries are evaluated separately and averaged
to get the final results.

Figure 10: Results on the IJB-B dataset across gallery sets S1 and S2.
Left: ROC (higher is better); Middle: DET (lower is better); Right: CMC
(higher is better).

The IJB-B dataset is an extension of IJB-A, having $1,845$ subjects with
$21.8$K still images (including $11,754$ face and $10,044$ non-face) and $55$K
frames from $7,011$ videos. We evaluate the models on the standard 1:1
verification protocol (matching between the Mixed Media probes and two
galleries) and 1:N identification protocol (1:N Mixed Media probes across two
galleries).

We observe a similar behaviour to that of the IJB-A evaluation. For the
comparison between different training sets (Table VII and Figure 10), the
models trained on VGGFace2 significantly surpass the ones trained on MS1M, and
the performance can be further improved by integrating the advantages of the
two datasets. In addition, SENet’s superiority over ResNet-50 is evident in
both verification and identification with the two training settings (i.e.
trained from scratch and fine-tuned). Moreover, we also compare to the results
reported by others on the benchmark [23] (as shown in Table VII), and there is
a considerable improvement over their performance for all measures.

###  V-E Experiments on IJB-C

Training dataset | Arch. | 1:1 Verification TAR | 1:N Identification TPIR
---|---|---|---
|  | FAR=$1E-5$ | FAR=$1E-4$ | FAR=$1E-3$ | FAR=$1E-2$ | FPIR=$0.01$ | FPIR=$0.1$ | Rank-$1$ | Rank-$5$ | Rank-$10$
VGGFace2 | ResNet-50 | $0.734$ | $0.825$ | $0.900$ | $0.950$ | $0.735\pm 0.022$ | $0.830\pm 0.021$ | $0.898\pm 0.017$ | $0.939\pm 0.013$ | $0.953\pm 0.009$
VGGFace2_ft | ResNet-50 | $0.749$ | $0.846$ | $0.913$ | $0.958$ | $0.749\pm 0.021$ | $0.849\pm 0.018$ | $0.908\pm 0.022$ | $0.946\pm 0.014$ | $0.958\pm 0.011$
VGGFace2 | SENet | $0.747$ | $0.840$ | $0.910$ | $0.960$ | $0.746\pm 0.018$ | $0.842\pm 0.022$ | $0.912\pm 0.017$ | $0.949\pm 0.010$ | $0.962\pm 0.007$
VGGFace2_ft | SENet | $\mathbf{0.768}$ | $\mathbf{0.862}$ | $\mathbf{0.927}$ | $\mathbf{0.967}$ | $\mathbf{0.763\pm 0.018}$ | $\mathbf{0.865\pm 0.018}$ | $\mathbf{0.914\pm 0.020}$ | $\mathbf{0.951\pm 0.013}$ | $\mathbf{0.961\pm 0.010}$
Maze et al. [14] |  | $0.600$ | $0.750$ | $0.860$ | $0.950$ | $0.450$ | $0.620$ | $0.790$ | $0.870$ | $0.900$

TABLE VIII:  Performance evaluation on the IJB-C dataset. A higher value is
better. The results of [14] are read from the curves reported in the paper.
Note, [14] has a different evaluation for the verification protocol where
pairs generated from different galleries are evaluated separately and averaged
to get the final results.

Figure 11: Results on the IJB-C dataset across gallery sets G1 and G2.
Left: ROC (higher is better); Middle: DET (lower is better); Right: CMC
(higher is better).

The IJBC dataset is a further extension of IJB-B, including $3531$ subjects
with $31.3$K still images and $117.5$K frames from $11,779$ videos. We
evaluate the models on the standard 1:1 verification protocol and 1:N
identification protocol. Results are shown in Table VIII and Figure 11.
Compared to the results reported in [14], there is a considerable improvement
for all measures.

##  VI Conclusion

In this work, we have proposed a pipeline for collecting a high-quality
dataset, VGGFace2, with a wide range of pose and age. Furthermore, we
demonstrate that deep models (ResNet-50 and SENet) trained on VGGFace2,
achieve state-of-the-art performance on the IJB-A, IJB-B and IJB-C benchmarks.
The dataset and models are available at
https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.

## Acknowledgment

We would like to thank Elancer and Momenta for their part in preparing the
dataset222http://elancerits.com/ https://momenta.ai/. This research is based
upon work supported by the Office of the Director of National Intelligence
(ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract
number 2014-14071600010. The views and conclusions contained herein are those
of the authors and should not be interpreted as necessarily representing the
official policies or endorsements, either expressed or implied, of ODNI,
IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce
and distribute reprints for Governmental purpose notwithstanding any copyright
annotation thereon.

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