# A Latent Fingerprint in the Wild Database

Xinwei Liu, Kiran Raja, Renfang Wang, Hong Qiu, Hucheng Wu, Dechao Sun,
Qiguang Zheng, Nian Liu, Xiaoxia Wang, Gehang Huang, Raghavendra Ramachandra,
Christoph Busch  Figure 1: Our contribution of Latent Fingerprint In the Wild
(LFIW) dataset compared to previous works. This paper was supported by the
National Natural Science Foundation of China (Grant No. 62106228, 61906170),
Zhejiang Natural Science Foundation (Grant No. LQ22F020003), Ningbo Natural
Science Foundation (Grant No. 2021J175), and the Ningbo Yongjiang Talent
Introduction Programme 2021.Xinwei Liu, Renfang Wang, Hong Qiu, Hucheng Wu,
Qiguang Zheng are with the College of Big Data and Software Engineering,
Zhejiang Wanli University (ZWU), 8 Qianhu South Road, Ningbo, Zhejiang,
China.Kiran Raja, Raghavendra Ramachandra, Christoph Busch are with Norwegian
University of Science and Technology (NTNU), 2815 Gjøvik, Norway.Dechao Sun is
with the College of Digital Technology and Engineering, Ningbo University of
Finance & Economics, Ningbo, China.Nian Liu and Xiaoxia Wang are with the
Library and Learning Resource Center, Zhejiang Wanli University (ZWU), 8
Qianhu South Road, Ningbo, Zhejiang, China.Gehang Huang is with the HDU-ITMO
Joint Institute, Hangzhou Dianzi University, Hangzhou, China.Manuscript
received XXXX XX, 2023; revised XXXX XX, 2023.

###### Abstract

Latent fingerprints are among the most important and widely used evidence in
crime scenes, digital forensics and law enforcement worldwide. Despite the
number of advancements reported in recent works, we note that significant open
issues such as independent benchmarking and lack of large-scale evaluation
databases for improving the algorithms are inadequately addressed. The
available databases are mostly of semi-public nature, lack of acquisition in
the wild environment, and post-processing pipelines. Moreover, they do not
represent a realistic capture scenario similar to real crime scenes, to
benchmark the robustness of the algorithms. Further, existing databases for
latent fingerprint recognition do not have a large number of unique
subjects/fingerprint instances or do not provide ground truth/reference
fingerprint images to conduct a cross-comparison against the latent. In this
paper, we introduce a new wild large-scale latent fingerprint database that
includes five different acquisition scenarios: reference fingerprints from (1)
optical and (2) capacitive sensors, (3) smartphone fingerprints, latent
fingerprints captured from (4) wall surface, (5) Ipad surface, and (6)
aluminium foil surface. The new database consists of 1,318 unique fingerprint
instances captured in all above mentioned settings. A total of 2,636 reference
fingerprints from optical and capacitive sensors, 1,318 fingerphotos from
smartphones, and 9,224 latent fingerprints from each of the 132 subjects were
provided in this work. The dataset is constructed considering various age
groups, equal representations of genders and backgrounds. In addition, we
provide an extensive set of analysis of various subset evaluations to
highlight open challenges for future directions in latent fingerprint
recognition research.

###### Index Terms:

Biometrics, latent fingerprints, fingerprint recognition, database,
performance evaluation.

##  I Introduction

Latent fingerprints were first reported to convict a suspect as evidence in
1893 [1]. Over the years, latent fingerprints have been regarded as one of the
most commonly and broadly used sources of evidence in crime scenes, digital
forensics, law enforcement, etc [1]. Latent fingerprints can be left on
various surfaces when a finger makes contact with an object. The manner in
which a finger touches the surface of an object has a significant impact on
the latent fingerprint quality (e.g., sharpness, contrast, and visible area).
There is a long history that latent fingerprint recognition was performed by
latent examiners before the development of Automated Fingerprint
Identification System (AFIS). In recent years, latent AFIS has become one of
the most commonly used technologies by law enforcement agencies worldwide [2].
More than 300,000 latent fingerprint identification demands were sent to the
FBI over the United States only in 2020 [3].

Unlike rolled and slap fingerprints (reference fingerprint images acquired
using standard fingerprint capture devices), latent fingerprints are captured
under unconstrained and unsupervised conditions. Low quality, partial
visibility, and the absence of satisfactory number and quality of minutiae
points are common issues faced in latent fingerprint recognition. The National
Institute of Standards and Technology (NIST) announced two fingerprint vendor
technology evaluations (FpVTE) in 2003 and 2012, respectively, [4] to advance
the research on latent fingerprint recognition. FpVTE was intended for the
evaluation of fingerprint system performance to meet the requirements for
real-world applications for both reference and latent fingerprints. In the
latest FpVTE2012, the lowest False Negative Identification Rate (FNIR) and
False Positive Identification Rate (FPIR) were reported as $1.9\%$ and $0.1\%$
by the top-performing AFIS for reference fingerprints [5]. However, the best
identification rate was only $67.2\%$ for latent fingerprints during the NIST
Evaluation of Latent Fingerprint Technologies: Extended Feature Sets (ELFT-
EFS) [6]. The major difference in recognition performance between the
reference and latent fingerprints is mainly caused by the low fingerprint
quality of the ridge-and-valley structures in latent fingerprints. It is
obvious that the further development of robust and high-accuracy latent
fingerprint recognition systems is necessary, however a progress is currently
limited by the sparse access to openly available datasets.

In the past decade, several studies have focused on developing latent
fingerprint recognition algorithms [7]. However, the performance evaluation of
these methods was conducted on only a few databases, such as the NIST SD27
Database [8], IIIT-D Latent Fingerprint Database [9], and Tsinghua Overlapped
Latent Fingerprint Database [10]. Despite being valuable, these databases have
major shortcomings:1) a small number of subjects respectively finger instances
and latent fingerprint samples, 2) a constrained acquisition environment, and
3) limited availability. Moreover, one of the most commonly used latent
fingerprint databases NIST SD27 Database has been withdrawn, making the
development and performance evaluation of latent fingerprint recognition even
more difficult.

In this paper, we first review all existing studies on latent fingerprint
recognition to provide an overview to the reader. We then provide an extensive
analysis of algorithms that are relevant for segmentation, minutiae
extraction, and the comparison of latent fingerprints both within and across
sensors. From a review of existing works, we note that latent fingerprint
recognition algorithms have rarely been tested on large-scale datasets [11].
To the best of our knowledge, there is no large-scale latent fingerprint in
the wild database that containing both reference fingerprints (ground truth)
and latent fingerprints acquired from different surfaces. Therefore, it is
necessary to establish a new large-scale latent fingerprint in the wild
database to meet the need for robust latent fingerprint recognition algorithm
development and evaluation.

Figure 2: Examples of comparison scores of the fingerprints from the proposed
LFIW database.

In order to address the limitations mentioned above, we provide the following
three major contributions in this paper:

  * •

Noting the non-availability of public datasets, a large-scale database of
latent fingerprints in the wild is presented in this work which is referred to
as ”Latent Fingerprint In the Wild” (LFIW). The dataset is collected in six
different scenarios, constituting a total of 13180 images of 132 subjects, and
is released along with this paper. This dataset contains various age groups
and equal representations of genders and backgrounds, making it a unique
dataset for the performance evaluation of latent fingerprint recognition
algorithms. As can be seen in Fig. 2, the comparison scores significantly
decrease from reference vs. reference comparison to latent vs. latent
comparison, which also indicates that the LFIW dataset is suitable for the
evaluation and development for the current and future latent fingerprint
recognition techniques. The LFIW dataset is available for academic research
purposes 111Upon acceptance of the paper..

  * •

Unlike other works, we also present a ground truth of fingerprints captured
using optical and capacitive sensors to conduct an analysis of contact-based
versus latent fingerprint comparison. In addition, owing to recent trends in
the use of smartphone-based fingerphotos in biometrics, we also introduce
fingerphoto images to benchmark the latent to the fingerphoto recognition.

  * •

A benchmark and independent evaluation of 5 state-of-the-art fingerprint
recognition methods and 1 latent fingerprint recognition approach is presented
to highlight the performance limitations of existing approaches. For each
method, a total of 118,620 mated comparison scores and 173,593,780 non-mated
comparison scores were generated for performance evaluation to derive
statistically significant conclusions

The paper is organized as follows. Section II provides an overview of past
studies which are related to our work in latent fingerprint recognition and
databases. We introduce the large-scale LFIW database where the details of the
whole dataset are illustrated in Section III. The evaluated fingerprint
recognition algorithms are introduced in Section IV, followed by a detailed
discussion of benchmarking results in Section V. Finally, Section VI draws the
conclusions.

##  II Related works

Latent fingerprint recognition is a complicated process and the accuracy is
generally low. As noted from Fig. 2, one can note that the comparison scores
for the mated samples drop heavily from capacitive to latent fingerprint
comparison. Compensation for human examiner supervision (or semi-automatic)
can increase the accuracy of latent fingerprint recognition (see Fig. 3 for an
example of common latent fingerprint recognition workflow). Such a workflow
has a significant difference from the common AFIS operation mode (e.g. border
control, mobile unlock and payment, etc.). With the rapid development of
biometric technology, more and more fully automated latent fingerprint
recognition algorithms have been proposed. There are three steps in the
automated latent fingerprint recognition system: segmentation, minutiae
extraction, and comparison. A brief review of existing approaches for these
three steps is given below and the detailed summary is illustrated in Table
II-D. Prior to discussing each of the components, we also discuss present
available datasets for latent fingerprint recognition.

Figure 3: Latent fingerprint recognition with human examiner supervision.

[sansboldbw]  DBs Unique Subjects Total Size Latent Size Finger photo included
Reference included Multiple Surface Availability NIST SD27 [8] N/A 258 258 ✗ ✓
✗ ✗ WVU [12] 35 449 449 ✗ ✓ ✗ ✗ MOLF [13] 100 6,000 6,000 ✗ ✗ ✗ ✓ TOLF [10] 10
112 100 ✗ ✗ ✗ ✓ IIIT-D [9] 15 1,046 1,046 ✗ ✓ ✗ ✓ SLF [14] 30 1,140 1,140 ✗ ✓
✗ ✓ ELFT-EFS [15] 837 1,114 1,066 ✗ ✗ ✗ ✗ IIID-MSLFD [16] 51 1,571 1,080 ✗ ✓ ✓
✓ Proposed - LFIW 132 13,180 9,224 ✓ ✓ ✓ ✓∗

TABLE I: Latent fingerprint databases.

∗The proposed database will be publicly available upon the acceptance of this
paper.

###  II-A Latent fingerprint databases

There are several existing latent fingerprint databases available for
performance evaluation, such as, West Virginia University (WVU) database [15],
Multisensor Optical and Latent Fingerpr (MOLF) database [13], Tsinghua Latent
Overlapped Fingerprint (TOLF) database [10], and so on. A list of commonly
used latent fingerprint databases is given in Table I. However, NIST SD27 has
been withdrawn and is no longer available.

As we can see from Table I, the size of existing latent fingerprint databases
is small and the simulation of real-world scene latent fingerprints is a very
challenging issue. Unfortunately, the only real crime scenes database NIST
SD27 has been withdrawn. To develop latent fingerprint recognition techniques,
creating a new and challenging database is needed. Therefore, a new database
that meets the following requirements is desirable:

  1. 1.

Large-scale database including large number of unique finger instances and not
just multiple fingers from a few unique subjects;

  2. 2.

Real-world scenarios where latent fingerprints vary in terms of quality,
resolution and material surfaces;

  3. 3.

Both reference and latent fingerprints are available for each unique
fingerprint instance in contact-less and contact-based scenarios;

  4. 4.

Public availability of dataset for academic research under different latent
and cross-sensor (latent-vs-contact-based) protocols.

Compared to other existing latent fingerprint databases, the proposed LFIW
dataset not only has the largest number of total fingerprint samples (13,180)
from various scenarios but also has the largest amount of unique fingerprint
instances (1318 from 132 subjects). The database further meets the criteria
mentioned above.

###  II-B Latent fingerprint segmentation techniques

Latent fingerprint segmentation can be defined as the separation of the
fingerprint region from the entire image. Segmentation methods with high
accuracy can not only reduce the computational complexity but also usually
improve the minutiae extraction performance. There are two types of
segmentation tasks: separating non-overlapping and overlapping latent
fingerprints. For non-overlapped latent fingerprint segmentation task, an
extended directional total variation model was developed by Zhang et al. [17]
to search for and separate latent fingerprints from the background. Cao et al.
[18] presented a dictionary based approach to segment latent fingerprints as
well as improve their quality. Many machine-deep learning (ML/DL)-based latent
fingerprint segmentation approaches have been developed over the past few
years. Patches from the region of interest of an image are trained in a
convolutional neural network (CNN) and used for segmentation by Stojanovic et
al. [19]. A foreground (latent fingerprint) and background classification
method was developed by Sankaran et al. [20], which takes advantage of random
decision forest. Nguyen et al. [21] introduced a CNN-based latent fingerprint
segmentation algorithm (SegFinNet) to compensate for the insufficient
performance of existing Commercial Off-The-Shelf (COTS) latent fingerprint
recognition methods. Compared to non-overlapped latent fingerprint
segmentation, separating overlapped fingerprints from each other and from the
background is challenging. Chen et al. [22] applied local Fourier transform
and relaxation labelling to segment overlapped fingerprints. To overcome the
shortcomings of relaxation labelling-based methods, Zhao and Jain [23]
developed a zero-pole model, Legendre polynomial, 2D Fourier Expansion, and
monomial basis function for overlapped fingerprint segmentation. An adaptive
neuro-fuzzy inference system classifier was used for overlapping fingerprint
segmentation by Jeyanthi et al. [24]. Stojanovic et al. [25] combined neural
networks and Fourier analysis to separate the overlapping fingerprints.

###  II-C Latent fingerprint minutiae extraction techniques

Many fingerprint minutiae extraction methods have been developed in the past,
however, the number of minutiae extraction algorithms that are especially used
for the latent fingerprints is limited. Su and Srihari [26] developed a latent
fingerprint minutiae extraction approach using a regression Gaussian process
model to estimate the location of finger core points and orientation fields.
Sankaran et al. [27] presented to classify minutia or non-minutia regions in a
latent fingerprint by using stacked denoising sparse auto-encoders. Tang et
al. [28] used a fully connected CNN to extract minutiae from the complicated
background so that latent fingerprint segmentation and quality enhancement are
no longer needed in this approach.

###  II-D Latent fingerprint comparison pipeline

It is not a simple task to find a match between an unknown fingerprint and a
fingerprint in a big database, while this becomes even more difficult for
latent fingerprint. Jain and Feng [29] combined extended fingerprint features
and minutiae to perform latent fingerprint comparison. Paulino et al. [15]
applied Descriptor-Based Hough Transform (DBHT) to compare reconstructed
orientation fields in two latent fingerprints. Cao and Jain [1] proposed to
generate two minutiae templates (obtained from CNN-based and dictionary-based
ridge flow, respectively) and one texture template (virtual minutiae) for
latent fingerprint comparison. In addition to use minutiae for matching, pores
were also used by Nguyen and Jain [30] to increase the accuracy of latent
fingerprint comparison.

[sansboldbw]  Reference Technique DB Evaluation Non-overlapped latent
fingerprint segmentation methods Zhang et al. [17] Extended directional total
variation model NIST SD27 Subjective inspection Cao et al. [18] Course and
fine ridge structure dictionary SD27& WVU R1-IA=0.61(SD27)/0.70(WVU)
Stojanovic et al. [19] CNN-based FVC2002 [31] EC=0.039(noisy)/0.037(regular)
Sankaran et al. [20] Random decision forest NIST SD4/27 IIITD-CLF [16]
R50-IA=0.723 Nguyen et al. [21] Fully convolutional neural network NIST SD27
WVU [15] MDR=0.026(SD27)/0.131(WVU), FDR=0.164(SD27)/0.053(WVU) Overlapped
latent fingerprint segmentation methods Chen et al. [22] Fourier transform and
relaxation labelling FVC2002 [31] Insufficient for singular points Zhao and
Jain [23] Joint orientation modeling NIST SD27 FVC2002 [31] Better manual
marking minutiae Jeyanthi et al. [24] Adaptive neuro fuzzy inference system
NIST SD27 FVC2006 [32] SR=0.924, CR=0.886, AC=0.92 Stojanovic et al. [25]
Neural networks and Fourier analysis TOLF [10] R1-IA=0.94(S)/0.91(M)/0.90(L)
Latent fingerprint minutiae extraction methods Su et al. [26] Regression
Gaussian process model NIST SD27/4 AC=0.845 Sankaran et al. [27] Stacked
denoising sparse autoencoders NIST SD27 R10-IA=0.3361 Tang et al. [28] Fully
CNN NIST SD27 AC=0.534 Latent fingerprint comparison methods Jain et al. [29]
Extended fingerprint features and minutiae NIST SD27/4 R1-IA=0.74 Paulino et
al. [15] DBHT SD27& WVU R1-IA is better then VeriFinger SDK Cao et al. [1] 2
minutiae and 1 texture templates SD27& WVU R1-IA=0.647(SD27)/0.753(WVU) Nguyen
et al. [30] Using both minutiae and pores NIST SD30
R1-IA=0.964(Min)/0.981(M+P)

TABLE II: Latent fingerprint recognition techniques.

Note: FAR represents the False Acceptance Rate, TAR represents the True
Acceptance Rate, MDR represents the Missed Detection Rate, FDR represents the
False Detection Rate, R1-IA represents the Rand 1 Identification Accuracy, EC
represents the Error Coefficient, R50-IA represents the Rank-50 identification
accuracy, SR represents the Separation Rate, CR represents the Classification
Rate, AC represents the accuracy, S represents small, M represents medium, L
represents large, AEE represents the Average Estimation Error (in degrees),
R10-IA represents the Rank-10 identification accuracy, Min represents the
minutiae, and M+P represents the minutiae plus pores.

##  III Latent Fingerprint in the Wild Database

As noted in the previous studies, the performance evaluation of existing
latent fingerprint recognition techniques is mainly based on only one or
certain databases, which are usually limited in size, diversity of image
acquisition devices, image quality, and realistic capture environment. The
best way to evaluate the performance of a latent fingerprint recognition
algorithm is to challenge it using different databases, image acquisition and
testing protocols. In order to overcome these limitations and provide a new
database for performance evaluation under real-world scenarios with high image
quality, the LFIW database created in this work consists of six subsets of
which two subsets are traditional fingerprints, three latent fingerprints and
one fingerphoto set as provided below:

  1. 1.

R-opt: Reference fingerprints from optical sensor;

  2. 2.

R-cap: Reference fingerprints from capacitive sensor;

  3. 3.

Smt: Smartphone fingerphotos;

  4. 4.

L-wall: Latent fingerprints captured from wall surface;

  5. 5.

L-ipad: Latent fingerprints captured from Ipad surface;

  6. 6.

L-alum: Latent fingerprints captured from aluminum foil surface.

Detailed information of the LFIW database is further provided in Table III.
All fingerprint images have been cropped and rotated to remove the background
in order to avoid unnecessary variables and facilitate the following
processing steps (e.g. enhancement, minutiae extraction, etc.). Examples of
the R-opt, R-cap, Smt, L-wall, L-ipad, and L-alum images are illustrated in
Fig. 1 red dotted block.

###  III-A Reference fingerprint images: R-opt and R-cap

For each of the 132 subjects in the LFIW database, two enrolment images were
captured by using two professional fingerprint acquisition sensors: one
optical fingerprint sensor and one capacitive sensor. The optical sensor is
ZKTeco Live10R fingerprint capture device and the capacitive sensor is Bingup
FPW-A360 fingerprint capture device. The original size of the fingerprint
images from the optical sensor is $288\times 375$ pixels (106 KB) and is
$256\times 360$ pixels (91 KB) for the capacitive sensor. All reference
fingerprint images are in 500 ppi. There are a total of $(132\quad
subjects\times 10\quad fingers-2\quad lost)\times 2\quad sensors\times 2\quad
enrolment=5272$ reference fingerprint images in the LFIW database.

###  III-B Smartphone fingerphoto images: Smt

All smartphone fingerphoto images were taken by Huawei Honor20 smartphone
(48+8+2 megapixel triple camera). All subjects were asked to place each of
their ten fingers on a white background under additional white light source.
The acquisition distance to the fingers and the focus were controlled and the
build-in flash has been turned off. The original size of the fingerprint
images is $3000\times 4000$ pixels ($\sim$2MB) and the ppi is 96 by default.
There are a total of $132\quad subjects\times 10\quad fingers-2\quad
lost=1318$ smartphone fingerprint images in the LFIW database.

###  III-C Wall surface latent fingerprint images: L-wall

In order to simulate the latent fingerprints captured on the wall in a real
crime scene (indoor environment, such as office, bank, school, etc.), subjects
were required to touch all 10 fingers on an office desk partition wall to
leave their fingerprints on the wall. Copper powder was used to make
fingerprints visible and wall latent fingerprint images were taken by Iphone 8
plus smartphone (12 megapixel dual camera). Additional white light source was
used and the acquisition distance to the fingerprints was controlled while the
build-in flash has been turned off. The original size of the wall latent
fingerprint images is $3024\times 4032$ pixels ($\sim$2.5MB) and the ppi is 72
by default. There are a total of $132\quad subjects\times 10\quad
fingers-2\quad lost=1318$ wall surface latent fingerprint images in the LFIW
database.

###  III-D Ipad surface latent fingerprint images: L-ipad

In order to simulate the latent fingerprints captured on the surface of
electronic devices as well as on the glasses in a real crime scene, subjects
were required to touch all 10 fingers on an Ipad screen surface (without
protective film) to leave their fingerprints on the Ipad screen. Copper powder
was used to make fingerprints visible and latent fingerprint images were taken
by the same Iphone 8 plus smartphone. The acquisition setups and images
properties are the same as the L-wall. Since additional white light source was
used, the screen reflection was avoided as much as possible during the
acquisition process. There are a total of $132\quad subjects\times 10\quad
fingers-2\quad lost=1318$ Ipad screen surface latent fingerprint images in the
LFIW database.

###  III-E Aluminum foil surface latent fingerprint images: L-alum

In order to simulate the latent fingerprints captured on the (deformable)
metal surface in a crime scene, subjects were required to touch all 10 fingers
on an aluminum foil surface to leave their fingerprints on the foil. Copper
powder was again used to make fingerprints visible and aluminum foil surface
latent fingerprint images were taken by the same Iphone 8 plus smartphone. The
acquisition setups and images properties are the same as the L-wall. Since
additional white light source was used, the aluminum foil reflection was
avoided as much as possible during the acquisition process. There are a total
of $132\quad subjects\times 10\quad fingers-2\quad lost=1318$ aluminum foil
surface latent fingerprint images in the LFIW database.

###  III-F Fingerprint images preprocessing

All Smt, L-wall, L-ipad, and L-alum original images have been cropped and
rotated manually for further processing. Moreover, all preprocessed
fingerprint images (JPEG format) have a 500 ppi version, a gray-scale 500 ppi
version, a gray-scale 500 ppi PNG format version, and a gray-scale 500 ppi PGM
format version.

[sansboldbw]  Property Information Number of subjects 132 Finger instances per
subject 5 fingers per hand $\times$ 2 hands Gender 74 Males + 58 Females Age
groups 18-35: 106, 36-55: 19, 56-70: 7 Number of images 100 per subject
$\times$ 132 $=$ 13180 ∗

TABLE III: Properties of the ’Latent Fingerprint in the Wild’ database.

∗ one of the subjects (worker) lost two Thumb fingers.

##  IV Fingerprint recognition algorithms

As described in the previous sections, a number of existing state-of-the-art
fingerprint recognition algorithms are evaluated on the new LFIW database.
Meanwhile, different versions of minutiae/features have been generated by
these evaluated algorithms and stored in the benchmark databases for further
evaluation. In this section, we briefly discuss the algorithms that were
tested on the LFIW database.

####  IV-1 NIST Biometric Image Software (NBIS) [33]

The NBIS is one of the most well-known fingerprint recognition toolkits that
can be freely used and distributed. Two components are used for the
performance evaluation: MINDTCT and BOZORTH3. MINDTCT is a minutiae detector
and it can automatically locate and record ridge endings and bifurcations in a
fingerprint image. BOZORTH3 is a fingerprint comparison algorithm and it is
minutiae-based. It accepts minutiae generated by the MINDTCT algorithm. All
extracted minutiae from MINDTCT are stored in the benchmark databases and can
be used for other fingerprint-comparison algorithms in case needed.

####  IV-2 Minutia Cylinder-Code (MCC) fingerprint recognition SDK [34, 35]

The so-called ’cylinder’ is a 3D data structure containing minutiae distances
and angles. Any standardized minutiae position and direction (e.g. ISO/IEC
19794-2 [36]) can be used as mandatory pre-condition to establish the
cylinder. Instead of designing complex metrics to calculate local similarities
and generate the comparison score, a very simple algorithm is applied in MCC
by taking advantages of the cylinder invariance. MCC uses ISO/IEC minutiae
information to generate its own minutiae template for fingerprint comparison
[37, 38].

####  IV-3 VeriFinger fingerprint recognition SDK [39]

VeriFinger is a commercial fingerprint recognition software designed for
biometric systems developers and integrators by Neurotechnology [39]. The
software can conduct fast fingerprint comparison in 1-to-1 and 1-to-many
modes. The VeriFinger algorithm is based on deep neural networks and follows
the commonly accepted fingerprint recognition scheme, which uses a set of
minutiae along with a number of proprietary algorithmic solutions that enhance
system performance and reliability. VeriFinger can produce its own minutiae
template for fingerprint comparison. It has also been submitted to the FVC-
onGoing [37, 38] framework and has reached NIST MINEX compliance.

####  IV-4 MinutiaeNet minutiae extractor [40]

MinutiaeNet can perform fully automatic latent fingerprint minutiae extraction
by using two independent deep neural networks. The first network is named as
CoarseNet and it estimates the minutiae score map and minutiae orientation
based on CNN and fingerprint domain knowledge (enhanced image, orientation
field, and segmentation map). FineNet is the second network and it refines the
candidate minutiae locations based on the score map. MinutiaeNet has been
particularly tested on NIST SD27 latent fingerprint database and the
performance is better than several other state-of-the-art minutiae extraction
algorithms. However, MinutiaeNet needs to apply other methods for minutiae
comparison, such as MCC or BOZORTH3.

####  IV-5 MSU Latent Automatic Fingerprint Identification System [41]

MSU-LAFIS is an end-to-end latent fingerprint search system, which has five
main steps: 1) fingerprint region of interest segmentation, 2) segmented image
pre-processing, 3) feature extraction, 4) feature comparison, and 5)
comparison results generation. Two isolated feature extraction algorithms are
used to produce additional feature templates. In order to avoid an
insufficient number of extracted features from latent fingerprints (too small
area or very low image quality), the feature template can be established by
combining real extracted features and a group of generated virtual features.
Each latent fingerprint feature and its neighbourhood are used to obtain a
96-dimensional descriptor for feature comparison. The descriptor length of the
virtual feature is further compressed from 96 to 16 to increase processing
speed by using product quantization.

##  V Protocols, Results and Discussion

[sansboldbw]  Algorithm GMean GSTD IMean ISTD AUC(%) EER(%) ZFMR(%) FMR100(%)
FMR20(%) FMR10(%) NBIS 15.21 32.13 4.22 3.59 57.91 46.10 90.79 80.58 75.25
70.94 MCC 0.03 0.04 0.01 0.002 75.44 32.27 99.82 63.60 56.77 51.17 VeriFinger
162.66 128.21 6.79 5.73 85.44 22.82 55.08 30.78 29.12 27.53 MN-MCC -0.15 0.37
0.01 0.002 46.36 51.85 99.43 94.31 89.24 84.55 MN-NBIS 1.30 5.11 0.34 1.04
54.77 45.61 98.56 91.85 89.10 81.29 MSU 28.87 56.75 3.55 5.34 57.10 47.51
100.00 75.38 72.82 70.66

TABLE IV: Performance indicators measured on the LFIW database for the overall
comparison experiments.

Note: GMean: Mated scores distribution mean; GSTD: Mated scores distribution
standard deviation; IMean: Impostor scores distribution mean; ISTD: Non-mated
scores distribution standard deviation; AUC: Area under the ROC curve; EER:
Equal Error Rate; FMR100/20/10: False match rate when false non match rate is
1%/20%/10%.

With the newly introduced dataset, we also conduct an extensive evaluation by
introducing three different protocols. The first protocol is to establish the
baseline performance in traditional fingerprint capture devices (optical and
capacitive sensors). The second protocol is to evaluate the scenarios of
comparing the latent-vs-latent and latent fingerprint with traditional
contact-based fingerprints. The third protocol is to account for comparison of
latent fingerprints with contactless fingerprints derived from fingerphotos.
With our protocols, we cover all possible scenarios of relevance in real-world
use cases. Given the large scale of LFIW dataset, we also perform both
verification and identification experiments to provide the reader with an
understanding of the challenges and thereby suggest directions for future
works.

###  V-A Verification results - Overall

Before each of the protocols is considered, we provide an overall evaluation
of the dataset by combining all the images of LFIW dataset. In the overall
evaluation experiment, a total of 118,620 mated comparison scores and
173,593,780 non-mated comparison scores are generated. The Detection Error
Tradeoff (DET) curves of the overall comparison experiments for the LFIW
dataset are presented in Fig. 4 along with the detailed results for various
metrics in Table IV. Two algorithms, VeriFinger and MCC perform slightly
better than the average, however, the Equal Error Rate (EER) is 22.82% and
32.27% respectively. Even the MinutiaeNet and MSU-LAFIS which are particularly
designed for latent fingerprint recognition perform poorly with an EER of
51.85% (MinutiaeNet-MCC)/45.61% (MinutiaeNet-NBIS) and 47.51%, respectively.
We have therefore analyzed the causes of low performance and observe a high
Failure To Enrol Rate (FTER) for most of the selected algorithms. The overall
FTER are:
$FTER_{NBIS}=25.2\%,FTER_{MCC}=36.68\%,FTER_{VeriFinger}=40.78\%,FTER_{MN-
MCC}=28.1\%,FTER_{MN-NBIS}=26.01\%,FTER_{MSU-AFIS}=7.67\%$, and FTER for
different protocols are given in Table V in blue color. Such a high FTER for
the second and third protocol in Table V indicates the difficulty to extract
minutiae from images in LFIW making it a challenging dataset.

Although some minutiae (features) can be successfully extracted, the number of
high quality minutiae (features) might be insufficient for an eligible
comparison. MSU-LAFIS has the lowest FTER, NBIS and MinutiaeNet (both MCC and
NBIS) have the FTER lower than $30\%$. Since MSU-LAFIS is especially developed
for latent fingerprint, so it corresponds to our expectation that it can
handle more than $90\%$ of the latent fingerprints in the LFIW database. The
FTER for VeriFinger reaches more than $40\%$ which means that almost half of
the latent fingerprints in the LFIW database cannot be used for recognition or
identification when using VeriFinger algorithm. On one hand, the high FTER
from selected non-latent-oriented methods indicates that the latent
fingerprints in the LFIW database are much more difficult to be processed
compared to existing fingerprints and latent fingerprints. Therefore, robust
latent fingerprint recognition algorithms are needed. On the other hand, the
pre-processing approaches from the selected algorithms can be optimized to be
able to handle latent fingerprints in the LFIW database.

Figure 4: DET curve of the overall comparison experiments for the LFIW
database.

(a) NBIS

(b) MCC

(c) VeriFinger

(d) MN-MCC (e) MN-NBIS (f) MSU-LAFIS Legend

Figure 5: Distributions of the overall comparison scores in the LFIW database

The distributions of the overall comparison scores from the selected
algorithms are illustrated in Fig. II-D. It can be observed that none of the
selected algorithms can well separate the mated scores and the non-mated
scores well. The highest frequency mated and non-mated scores are almost
overlapping for all the methods. Compared with other methods, the mated scores
of VeriFinger are more distributed far away from the non-mated scores (see
Fig. II-D (c)). This is probably due to the high FTER where latent
fingerprints have been rejected during the enrollment phase while it could
process reference fingerprints captured by using optical and capacitive
sensors. There are also a number of mated comparison scores separated far from
the non-mated scores for MSU-LAFIS in Fig. II-D (f), but the proportion is
lower than VeriFinger. The mated and non-mated scores of MinutiaeNet (both MCC
and NBIS) are highly overlapped. In addition to the distributions of the
comparison scores, we present the most important performance indicators
measured on the LFIW database for the overall comparison experiments in Table
IV. It can be noted that VeriFinger has the highest Area Under the ROC Curve
(AUC) value and lowest EER. Except VeriFinger, MCC out perform the rest of the
algorithms, however, its AUC and EER is still far from indicating the
properties of a robust latent fingerprint recognition system. Nevertheless, it
is difficult to distinguish whether those higher mated comparison scores are
from reference comparisons or latent comparisons by only looking at the
overall experimental results. Therefore, in the following parts we will
investigate the results for reference and latent comparisons, respectively.

(a) NBIS

(b) MCC

(c) VeriFinger

(d) MN-MCC

(e) MN-NBIS

(f) MSU-LAFIS

Legend

Figure 6: DET curves of the NBIS, MCC, VeriFinger, MinutiaeNet-MCC,
MinutiaeNet-NBIS, and MSU-LAFIS comparison experiments for the LFIW database

###  V-B Protocol I: Verification results - Traditional Sensors

We illustrate the DET curves of the NBIS, MCC, VeriFinger, MinutiaeNet-MCC,
MinutiaeNet-NBIS, and MSU-LAFIS comparison experiments for the LFIW database
in Fig. 6 and the most important performance indicators measured for the
selected algorithms in Table V. From Fig. 6 and Table V we can observe that
the reference comparisons (e.g. R1-opt to R2-opt, R2-opt to R2-cap) have
better performance than latent comparisons (e.g. R2-opt to L-wall, L-ipad to
L-alum). Except for VeriFinger, the best performance in reference comparisons
is from ’R1-opt to R2-opt (reference fingerprints from optical sensor session
1 vs. reference fingerprints from optical sensor session 2)’ (see blue dashed
lines with square markers in Fig. 6). The comparisons ’R1-cap to R2-cap’ give
the best performance for VeriFinger. Although the comparisons ’R1-cap to
R2-cap’ (red dashed lines with triangle markers) are between the same
acquisition device, the performance is lower than ’R1-opt to R2-opt’ for most
of the algorithms. It means that the utility of reference fingerprints from
optical sensor is better than the capacitive sensor measured by NBIS, MCC,
MinutiaeNet (both MCC and NBIS), and MSU-LAFIS. The performance of the
remaining reference comparisons are very similar. In Table V, the EER from the
’R1-cap to R2-cap’ comparison experiment is 0.82% for MCC, which is also
higher than the ’R1-opt to R2-opt’ experiment. However, both the above
mentioned two EERs are smaller (0.11% and 0.89%, respectively) than the NBIS.
Moreover, the difference between ’R1-opt to R2-opt’ and ’R1-cap to R2-cap’
comparison experiments for MCC are also 0.78 less, compared to NBIS. It means
that MCC has a better ability to process the reference fingerprints in the
LFIW database than NBIS. We can see from Table V that the overall EERs and
FMR100s for VeriFinger are lower than NBIS and MCC, which is the same as we
already discussed previously in the overall results section. By comparing Fig.
6 (d) and (e), as well as EER values for MinutiaeNet we can discover that,
NBIS has slightly better overall system performance than MCC when using the
extracted minutiae from MinutiaeNet. However, neither MCC nor NBIS can achieve
better system performance for MinutiaeNet compared to other fingerprint
recognition systems.

####  V-B1 Protocol Ia: Verification results - Traditional Cross-Sensors

We also study the cross-sensor recognition for the completeness of the
analysis by comparing the optical v/s capacitive sensors. However, the
recognition performance of ’R1-opt to R1-cap’ (orange dashed line with
triangle markers) and ’R1-cap to R2-opt’ (purple dashed line) are lower than
same-sensor comaprison for MSU-LAFIS (see Fig. 6 (f)).

###  V-C Protocol II: Verification results - Traditional v/s Latent

We further consider a realistic evaluation scenrio where the latents are to be
compared against traditional fingerprints. As noted from Table V, the overall
performance for latent v/s traditional fingerprints is low. The EER values are
around 50% and the FMR100 values are close to 100% for most of the reference
to latent and latent to latent comparisons from all algorithms. The results
suggest that comparing latent fingerprints in the LFIW database is a very
complex task for the selected algorithms. An interesting EER value 18.8% can
be observed from the ’L-ipad to L-alum’ comparison experiment for NBIS. This
EER value is much less than the others obtained from latent fingerprints
comparisons. Introspecting the comparison scores, we note a very high FTER in
NBIS for ’L-ipad to L-alum’ comparisons resulting in a misleading low EER. The
results also indicate that latent fingerprints captured from Ipad surface and
from aluminum foil surface are the most difficult ones for NBIS to extract
minutiae. We can see the EER values for VeriFinger are low for many reference
fingerprints to latent fingerprints comparison (e.g. EER for R1-opt to L-ipad
is 10.2% and latent fingerprints to latent fingerprints comparison (e.g. EER
for L-wall to L-ipad is 7.1%) experiments. After investigating the comparison
scores from these comparison experiments, we explore that the number of scores
is quite small. For example, there are 29 mated scores left for L-wall to
L-ipad comparison, there is only one mated score left for L-wall to L-alum
comparison experiments. All the above discovered atypical EER and FMR values
(e.g. low for NBIS and high for VeriFinger) are due to the high FTER that
already discussed previously (noted in Table V). Although MinutiaeNet has been
tested on NIST SD27 latent fingerprint database and MSU-LAFIS is especially
developed for latent fingerprints, they still fail to provide robust latent
fingerprints recognition performance on the LFIW database after looking at the
EER and FMR100 values in Table V.

TABLE V: Performance indicators measured on the LFIW database for the six
different algorithms.

Fingerprint Recognition Algorithm
---
| NBIS | MCC | VeriFinger | MN-MCC | MN-NBIS |  MSU-LAFIS
Protocol | FTER / EER | FTER / EER | FTER / EER | FTER / EER | FTER / EER | FTER / EER
GEER / FMR100 | GEER / FMR100 | GEER / FMR100 | GEER / FMR100 | GEER / FMR100 | GEER / FMR100
Reference to reference |
R1-opt to R2-opt | 0.21 / 3.34 | 0.28 / 2.21 | 0.50 / 1.20 | 0.19 / 30.66 | 0.24 / 18.14 | 0.00 / 3.29
3.83 / 4.53 | 2.49 / 2.52 | 1.32 / 1.21 | 32.57 / 63.26 | 16.67 / 45.73 | 3.29 / 4.00
R1-opt to R1-cap | 0.12 / 17.65 | 0.20 / 10.00 | 0.33 / 0.82 | 0.07 / 43.48 | 0.15 / 32.91 | 0.00 / 6.83
17.88 / 29.62 | 11.33 / 20.00 | 0.79 / 0.95 | 40.54 / 87.03 | 35.89 / 72.60 | 6.83 / 10.96
R1-opt to R2-cap | 0.12 / 15.00 | 0.19 / 8.53 | 0.32 / 1.02 | 0.08 / 41.56 | 0.15 / 32.05 | 0.00 / 11.93
16.66 / 26.39 | 8.26 / 17.48 | 1.06 / 1.01 | 44.61 / 82.45 | 33.94 / 72.22 | 11.93 / 16.75
R1-cap to R2-cap | 0.08 / 17.18 | 0.13 / 8.25 | 0.06 / 0.29 | 0.04 / 38.32 | 0.10 / 27.11 | 0.00 / 11.54
16.92 / 25.03 | 8.40 / 14.76 | 0.25 / 0.22 | 40.59 / 68.89 | 24.98 / 59.80 | 11.54 / 16.83
R1-cap to R2-opt | 0.10 / 17.14 | 0.17 / 10.15 | 0.28 / 0.55 | 0.06 / 44.48 | 0.13 / 33.81 | 0.00 / 7.23
17.97 / 31.73 | 11.35 / 21.07 | 0.61 / 0.56 | 43.22 / 85.73 | 36.51 / 76.07 | 7.23 / 11.44
Reference to Latent |
R1-opt to L-wall | 19.74 / 49.12 | 30.94 / 50.13 | 33.27 / 27.56 | 20.63 / 53.72 | 19.68 / 50.00 | 4.41 / 50.33
46.87 / 98.85 | 47.06 / 99.47 | 29.34 / 57.98 | 50.78 / 99.15 | 48.76 / 99.58 | 47.53 / 98.32
R1-opt to L-ipad | 18.22 / 50.00 | 28.67 / 48.60 | 29.15 / 10.23 | 16.60 / 51.96 | 17.92 / 50.00 | 4.17 / 49.13
49.70 / 98.77 | 44.71 / 99.00 | 20.57 / 30.64 | 46.33 / 98.93 | 52.90 / 99.76 | 46.79 / 96.71
R1-opt to L-alum | 20.64 / 52.32 | 34.38 / 47.15 | 35.49 / 29.43 | 21.36 / 54.22 | 21.08 / 50.00 | 5.04 / 49.89
40.48 / 100.00 | 45.86 / 97.84 | 27.11 / 73.00 | 56.39 / 99.63 | 49.27 / 99.26 | 45.44 / 97.71
R1-cap to L-wall | 17.99 / 50.65 | 26.48 / 51.17 | 27.31 / 31.33 | 16.45 / 55.26 | 16.55 / 50.00 | 4.06 / 50.89
39.81 / 99.24 | 55.92 / 99.35 | 28.56 / 100.00 | 50.72 / 100.00 | 53.61 / 99.45 | 48.31 / 97.97
R1-cap to L-ipad | 17.27 / 50.33 | 26.03 / 48.56 | 28.47 / 11.21 | 15.87 / 54.72 | 16.18 / 50.00 | 3.97 / 49.64
40.55 / 99.23 | 46.33 / 99.17 | 22.12 / 55.23 | 50.88 / 99.26 | 46.25 / 99.33 | 45.46 / 97.00
R1-cap to L-alum | 20.05 / 50.99 | 30.00 / 51.16 | 32.23 / 37.33 | 18.88 / 54.62 | 19.41 / 50.00 | 4.29 / 50.38
40.04 / 100.00 | 54.99 / 97.84 | 33.37 / 90.36 | 59.02 / 98.83 | 51.33 / 99.00 | 48.40 / 98.17
Latent to latent |
L-wall to L-ipad | 27.57 / 49.36 | 38.65 / 49.03 | 43.74 / 7.15 | 29.97 / 99.97 | 27.68 / 49.73 | 8.67/ 48.14
42.91 / 98.27 | 43.45 / 98.71 | 24.88 / 0.00 | 97.43 / 98.64 | 52.01 / 99.27 | 45.43 / 95.89
L-wall to L-alum | 27.91 / 56.32 | 39.06 / 52.34 | 44.86 / 16.78 | 31.63 / 99.95 | 28.99 / 50.00 | 9.22/ 47.30
55.37 / 100.00 | 48.64 / 98.62 | 27.19 / 50.00 | 96.09 / 99.23 | 49.16 / 98.76 | 44.54 / 94.95
L-ipad to L-alum | 29.49 / 18.84 | 40.95 / 48.61 | 45.81/ 17.42 | 32.09 / 100.00 | 29.92 / 49.13 | 10.27 / 47.09
31.05 / 100.00 | 55.83 / 98.94 | 29.01 / 50.00 | 99.33 / 99.45 | 53.31 / 98.37 | 50.00 / 88.22
Smartphone fingerphoto |
R1-opt to Smt | 23.48 / 50.00 | 33.25 / 50.74 | 38.07 / 46.08 | 26.61 / 54.63 | 23.89 / 49.22 | 6.42 / 49.96
46.34 / 98.58 | 45.20 / 98.53 | 42.93 / 97.27 | 49.84 / 99.21 | 50.01 / 99.62 | 45.76 / 98.96
R1-cap to Smt | 23.17 / 50.00 | 33.02 / 48.53 | 36.94 / 17.86 | 25.43 / 54.31 | 22.58 / 50.00 | 6.05 / 50.00
42.19 / 99.22 | 49.08 / 100.00 | 30.92 / 98.33 | 50.79 / 98.57 | 51.84 / 98.92 | 47.00 / 98.51
Smt to L-wall | 25.09 / 50.00 | 35.46 / 48.19 | 39.85 / 55.85 | 28.00 / 100.00 | 25.84 / 49.78 | 7.24 / 49.44
54.81 / 98.73 | 43.84 / 98.16 | 48.71 / 80.00 | 98.73 / 99.42 | 48.74 / 99.55 | 50.00 / 99.00
Smt to L-ipad | 25.89 / 49.74 | 36.04 / 52.46 | 40.37 / 52.25 | 29.44 / 99.78 | 26.38 / 49.43 | 7.55 / 49.84
47.35 / 98.87 | 55.70 / 98.31 | 44.33 / 97.26 | 96.24 / 98.55 | 45.19 / 99.72 | 44.44 / 98.51
Smt to L-alum | 26.87 / 50.00 | 36.95 / 55.25 | 40.86 / 55.52 | 30.08 / 99.98 | 26.75 / 50.00 | 7.68 / 49.84
52.74 / 100.00 | 49.89 / 100.00 | 57.05 / 100.00 | 97.07 / 99.12 | 53.03 / 99.16 | 46.38 / 99.19

###  V-D Protocol III: Verification results - Fingerphoto Comparisons

We further consider another protocol according to recent trends and benchmark
the performance for fingerphoto to latent fingerprint comparison.
Specifically, the protocol is aimed at using fingerphotos as a replacement to
traditional fingerprint capture from contact-based sensors. We therefore
evaluate, fingerphotos as reference and compare it to latent comparisons. From
Fig. 6 and Table V (in the bottom sector), one can note that the EER (more
than 50%) and FMR100 are high and indicate a challenging nature of smartphone
fingerphotos to latent comparison in the LFIW database.

(a) NBIS

(b) MCC

(c) VeriFinger

(d) MN-MCC

(e) MN-NBIS

(f) MSU-LAFIS

Legend

Figure 7: Cumulative match curves of the rank identification rates for NBIS,
MCC, VeriFinger, MinutiaeNet-MCC, MinutiaeNet-NBIS, and MSU-LAFIS
identification experiments for the LFIW database

###  V-E Identification results

In addition to demonstrating verification results, we also illustrate the
performance of identification results using Cumulative Match Curves (CMC) of
the Rank Identification (RI) rates (rank-10) for NBIS, MCC, VeriFinger,
MinutiaeNet-MCC, MinutiaeNet-NBIS, and MSU-LAFIS identification experiments
for the LFIW database as shown in Fig. 7. From Fig. 7 we can observe that the
overall RI rates for reference comparisons are higher than latent
identification. The RI rates for MCC, VeriFinger, MinutiaeNet-MCC, and MSU-
LAFIS can reach 100% at rank-6 to rank-10. While the RI rates for NBIS and
MinutiaeNet-NBIS are lower than 60% in all 10 ranks. Similar to the
verification results, ’R2-opt to R1-opt’ comparisons (gray dashed lines in
Fig. 7) remains the best performance but none of the algorithms can get RI
rates to 1 before rank-4.

In summary, none of the selected algorithms can provide high accuracy latent
fingerprints minutiae extraction and comparison by using fingerprints from the
LFIW database for both verification and identification scenarios. From the
experimental results above we can conclude that latent fingerprint recognition
by existing techniques is still very challenging and complex, especially for
latent fingerprints captured in the wild conditions. The results indicate
again the need for the development of robust latent fingerprint minutiae
extraction and recognition algorithms for wild latent fingerprints.

###  V-F Directions for future works

As illustrated from the experimental results discussed in the above sections,
the system performance of evaluated latent fingerprints recognition algorithms
does not meet the operational requirements. By looking at FMR100, we can see
from the overall results in Table IV or the reference/latent comparisons
results in Table V that the result is around 20% only for reference
fingerprints comparison experiments. For latent and smartphone fingerprints
comparison experiments, the FMR100 is higher than 90% and many of them are
even close to 100%. From a practical point of view, this behaviour would cause
a noticeable number of false matches and, as a result, a bigger number of
false non matches during latent fingerprints recognition at crime scene,
digital forensic, or law enforcement scenarios. This would not help to find
the real suspects. Therefore, the directions for the future works could be as
the following:

  * •

Given that the existing fingerprint recognition systems have very low
performance on smartphone and latent fingerprints captured from different
material, more accurate and robust algorithms are desired in order to overcome
the difficulties and challenges of wild latent fingerprints recognition.

  * •

As it also has been discussed that the FTER is relatively high for existing
fingerprint recognition algorithms, including systems that particularly for
latent fingerprints. Reliable and accurate latent fingerprints pre-processing
(e.g.segmentation, quality enhancement, minutiae orientation estimation, etc.)
and minutiae extraction approaches need to be developed.

  * •

While there exist quality assessment algorithms like NFIQ [42] for
fingerprints, quality assessment algorithms that can predict the recognition
performance for latent fingerprints are still missing.

  * •

As an additional direction, the performance of human examiner latent
fingerprints comparison could be investigated in a standardized manner to
discover the important factors in recognizing the latent fingerprints captured
in wild environment.

##  VI Conclusion

Latent fingerprints recognition has always been a complex and challenging task
with the availability of no public and large-scale datasets. In this work, we
have introduced LFIW, a new database of latent fingerprints in the wild. This
database has included six different acquisition scenarios: reference
fingerprints from (1) optical and (2) capacitive traditional fingerprint
sensors, (3) smartphone fingerphotos, latent fingerprints captured from (4)
wall surface, (5) Ipad surface, and (6) aluminium foil surface. The new
database consists of 1318 unique fingerprint instances captured in all of the
above-mentioned settings. A total of 2636 reference fingerprints from optical
and capacitive sensors, 1318 fingerphotos from smartphone, and 9224 latent
fingerprints from every 132 subjects are provided in this work. The presented
wild latent fingerprints database with large number of unique fingerprints
will be publicly available in order to allow researchers to benchmark their
algorithms in a free and sustainable manner to develop robust and accurate
latent fingerprints recognition algorithms. Additionally, a benchmark of
several existing state-of-the-art fingerprint recognition systems is also
provided in this paper to eliminate the limitations in the existing latent
fingerprints recognition methods, and to provide some directions for future
works in this research field.

## Acknowledgments

This paper is supported by the National Natural Science Foundation of China
(Grant No. 62106228, 61906170), Zhejiang Natural Science Foundation (Grant No.
LQ22F020003), Ningbo Natural Science Foundation (Grant No. 2021J175), and the
Ningbo Yongjiang Talent Introduction Programme 2021.

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