Latent fingerprints are important for identifying criminal suspects. However, recognizing a latent fingerprint in a collection of reference fingerprints remains a challenge. Most, if not all, of existing methods would extract representation features of each fingerprint independently and then compare the similarity of these representation features for recognition in a different process. Without the supervision of similarity for the feature extraction process, the extracted representation features are hard to optimally reflect the similarity of the two compared fingerprints which is the base for matching decision making. In this paper, we propose a new scheme that can model the pair-relationship of two fingerprints directly as the similarity feature for recognition. The pair-relationship is modeled by a hybrid deep network which can handle the difficulties of random sizes and corrupted areas of latent fingerprints. Experimental results on two databases show that the proposed method outperforms the state of the art.
Latent fingerprint enhancement for accurate minutiae detection
TCC Research and Development Labs, Technology Control Company（TCC研发实验室，技术控制公司）；University of New South Wales（新南威尔士大学）；Abasyn University Islamabad（阿巴辛伊斯兰堡大学）；Computer and Information Sciences College, Jouf University（焦夫大学计算机与信息科学学院）
Pyongyang Software Joint Development Center（平壤软件联合开发中心）
Fingerprints are popular among the biometric based systems due to ease of acquisition, uniqueness and availability. Nowadays it is used in smart phone security, digital payment and digital locker. Fingerprint recognition technology has been studied for a long time, and its recognition rate has recently risen to a high level. In particular, with the introduction of Deep Neural Network technologies, the recognition rate that could not be reached before was reached. In this paper, we propose a fingerprint detection algorithm used in a fingerprint recognition system.
Latent fingerprint enhancement is an essential pre-processing step for latent fingerprint identification. Most latent fingerprint enhancement methods try to restore corrupted gray ridges/valleys. In this paper, we propose a new method that formulates the latent fingerprint enhancement as a constrained fingerprint generation problem within a generative adversarial network (GAN) framework. We name the proposed network as FingerGAN. It can enforce its generated fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the corresponding ground-truth instance in terms of the fingerprint…
Pyongyang Software Joint Development Center（平壤软件联合开发中心）
Fingerprints are popular among the biometric based systems due to ease of acquisition, uniqueness and availability. Nowadays it is used in smart phone security, digital payment and digital locker. The traditional fingerprint matching methods based on minutiae are mainly applicable for large-area fingerprint and the accuracy rate would reduce significantly when dealing with small-area fingerprint from smart phone. There are many attempts to using deep learning for small-area fingerprint recognition, and there are many successes. But training deep neural network needs a lot of datasets for train…
Given a full fingerprint image (rolled or slap), we present CycleGAN models to generate multiple latent impressions of the same identity as the full print. Our models can control the degree of distortion, noise, blurriness and occlusion in the generated latent print images to obtain Good, Bad and Ugly latent image categories as introduced in the NIST SD27 latent database. The contributions of our work are twofold: (i) demonstrate the similarity of synthetically generated latent fingerprint images to crime scene latents in NIST SD27 and MSP databases as evaluated by the NIST NFIQ 2 quality meas…
Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latent fingerprint reconstruction model based on conditional generative adversarial networks (cGANs). Two modifications are applied to the cGAN to adapt it for the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information is considered in the genera…
Latent fingerprint matching is a very important but unsolved problem. As a key step of fingerprint matching, fingerprint registration has a great impact on the recognition performance. Existing latent fingerprint registration approaches are mainly based on establishing correspondences between minutiae, and hence will certainly fail when there are no sufficient number of extracted minutiae due to small fingerprint area or poor image quality. Minutiae extraction has become the bottleneck of latent fingerprint registration. In this paper, we propose a non-minutia latent fingerprint registration m…
We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet. SegFinNet takes a latent image as an input and outputs a binary mask highlighting the friction ridge pattern. Our algorithm combines fully convolutional neural network and detection-based approaches to process the entire input latent image in one shot instead of using latent patches. Experimental results on three different latent databases (i.e. NIST SD27, WVU, and an operational forensic database) show that SegFinNet outperforms both human markup for latents and the state-of-the-art laten…
Latent fingerprint recognition is not a new topic but it has attracted a lot of attention from researchers in both academia and industry over the past 50 years. With the rapid development of pattern recognition techniques, automated fingerprint identification systems (AFIS) have become more and more ubiquitous. However, most AFIS are utilized for live-scan or rolled/slap prints while only a few systems can work on latent fingerprints with reasonable accuracy. The question of whether taking higher resolution scans of latent fingerprints and their rolled/slap mate prints could help improve the i…
Fingerprint recognition is one of most popular and accuracy Biometric technologies. Nowadays, it is used in many real applications. However, recognizing fingerprints in poor quality images is still a very complex problem. In recent years, many algorithms, models...are given to improve the accuracy of recognition system. This paper discusses on the standardized fingerprint model which is used to synthesize the template of fingerprints. In this model, after pre-processing step, we find the transformation between templates, adjust parameters, synthesize fingerprint, and reduce noises. Then, we us…
LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification
Ramakrishna Mission Vidyamandira（拉马克里希纳使命学院）；Indian Institute of Information Technology (IIIT)（印度信息与技术学院）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学）
Typical evaluations of fingerprint recognition systems consist of end-to-end black-box evaluations, which assess performance in terms of overall identification or authentication accuracy. However, these black-box tests of system performance do not reveal insights into the performance of the individual modules, including image acquisition, feature extraction, and matching. On the other hand, white-box evaluations, the topic of this paper, measure the individual performance of each constituent module in isolation. While a few studies have conducted white-box evaluations of the fingerprint reader…
Michigan State University（密歇根州立大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Fingerprint recognition systems have been deployed globally in numerous applications including personal devices, forensics, law enforcement, banking, and national identity systems. For these systems to be socially acceptable and trustworthy, it is critical that they perform equally well across different demographic groups. In this work, we propose a formal statistical framework to test for the existence of bias (demographic differentials) in fingerprint recognition across four major demographic groups (white male, white female, black male, and black female) for two state-of-the-art (SOTA) fing…
Latent fingerprint has the practical value to identify the suspects who have unintentionally left a trace of fingerprint in the crime scenes. However, designing a fully automated latent fingerprint matcher is a very challenging task as it needs to address many challenging issues including the separation of overlapping structured patterns over the partial and poor quality latent fingerprint image, and finding a match against a large background database that would have different resolutions. Currently there is no fully automated latent fingerprint matcher available to the public and most literat…
New York University（纽约大学）；University of California, Riverside（加州大学河滨分校）
Fingerprint recognition has been utilized for cellphone authentication, airport security and beyond. Many different features and algorithms have been proposed to improve fingerprint recognition. In this paper, we propose an end-to-end deep learning framework for fingerprint recognition using convolutional neural networks (CNNs) which can jointly learn the feature representation and perform recognition. We train our model on a large-scale fingerprint recognition dataset, and improve over previous approaches in terms of accuracy. Our proposed model is able to achieve a very high recognition accu…
Khulna University of Engineering & Technology（库尔纳工程技术大学）
Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification system. Because minutiae matching are certainly the most well-known and widely used method for fingerprint matching, minutiae are local discontinuities in the fingerprint pattern. In this paper a fingerprint matching algorithm is proposed using some specific feature of the minutiae points, also the acquired fingerprint image is considered by minimizing its size by generating a corresponding fingerprint template for a large fingerprint database. The results achieved are compare…
Indian Institute of Technology Indore（印度理工学院印多尔分校）
With the development of high-resolution fingerprint scanners, high-resolution fingerprint-based biometric recognition has received increasing attention in recent years. This paper presents a pore feature-based approach for biometric recognition. Our approach employs a convolutional neural network (CNN) model, DeepResPore, to detect pores in the input fingerprint image. Thereafter, a CNN-based descriptor is computed for a patch around each detected pore. Specifically, we have designed a residual learning-based CNN, referred to as PoreNet that learns distinctive feature representation from pore …
Zhejiang Wanli University（浙江万里学院）；Norwegian University of Science and Technology（挪威科技大学）；Ningbo University of Finance & Economics（宁波财经学院）；Hangzhou Dianzi University（杭州电子科技大学）
The use of vision transformers (ViT) in computer vision is increasing due to limited inductive biases (e.g., locality, weight sharing, etc.) and increased scalability compared to other deep learning methods. This has led to some initial studies on the use of ViT for biometric recognition, including fingerprint recognition. In this work, we improve on these initial studies for transformers in fingerprint recognition by i.) evaluating additional attention-based architectures, ii.) scaling to larger and more diverse training and evaluation datasets, and iii.) combining the complimentary represent…
Fingerprint recognition has drawn a lot of attention during last decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/network, is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to sift descriptors and the higher layers capture higher frequency content of the signal. After extraction of scattering features, their dimensionality is …
Hochschule Darmstadt（达姆施塔特应用科学大学）；Freie Universität Berlin（柏林自由大学）
This work presents an automated touchless fingerprint recognition system for smartphones. We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system. Also, our implementation is made publicly available for research purposes. During a database acquisition, a total number of 1,360 touchless and touch-based samples of 29 subjects are captured in two different environmental situations. Experiments on the acquired database show a comparable performance of our touchless scheme and the touch-based baseline scheme…
Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a candidate list of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for…
Key Laboratory of Machine Perception (MOE), School of EECS, Peking University（北京大学信息科学技术学院机器感知与智能教育部重点实验室）
Minutiae play a major role in fingerprint identification. Extracting reliable minutiae is difficult for latent fingerprints which are usually of poor quality. As the limitation of traditional handcrafted features, a fully convolutional network (FCN) is utilized to learn features directly from data to overcome complex background noises. Raw fingerprints are mapped to a correspondingly-sized minutia-score map with a fixed stride. And thus a large number of minutiae will be extracted through a given threshold. Then small regions centering at these minutia points are entered into a convolutional n…
Fake fingerprint representation pose a severe threat for fingerprint based authentication systems. Despite advances in presentation attack detection technologies, which are often integrated directly into the fingerprint scanner devices, many fingerprint scanners are still susceptible to presentation attacks using physical fake fingerprint representation. In this work we evaluate five different commercial-off-the-shelf fingerprint scanners based on different sensing technologies, including optical, optical multispectral, passive capacitive, active capacitive and thermal regarding their suscepti…
Michigan State University（密歇根州立大学）；Dayalbagh Educational Institute（达亚尔巴格教育学院）；Saran Ashram Hospital（萨兰阿什拉姆医院）
With a number of emerging applications requiring biometric recognition of children (e.g., tracking child vaccination schedules, identifying missing children and preventing newborn baby swaps in hospitals), investigating the temporal stability of biometric recognition accuracy for children is important. The persistence of recognition accuracy of three of the most commonly used biometric traits (fingerprints, face and iris) has been investigated for adults. However, persistence of biometric recognition accuracy has not been studied systematically for children in the age group of 0-4 years. Given…
Indian Institute of Technology Indore（印度理工学院印多尔分校）
Fingerprints are one of the most widely explored biometric traits. Specifically, contact-based fingerprint recognition systems reign supreme due to their robustness, portability and the extensive research work done in the field. However, these systems suffer from issues such as hygiene, sensor degradation due to constant physical contact, and latent fingerprint threats. In this paper, we propose an approach for developing a contactless fingerprint recognition system that captures finger photo from a distance using an image sensor in a suitable environment. The captured finger photos are then p…
Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature…
This work observed the problem of fingerprint image recognition in the case of missing pixels from the original image. The possibility of missing pixels recovery is tested by applying the Compressive Sensing approach. Namely, different percentage of missing pixels is observed and the image reconstruction is done by applying commonly used approach for sparse image reconstruction. The theory is verified by experiments, showing successful image reconstruction and later person identification even if less then 90% of the image pixels is missing.
Fingerprint recognition requires a minimal effort from the user, does not capture other information than strictly necessary for the recognition process, and provides relatively good performance. A critical step in fingerprint identification system is thinning of the input fingerprint image. The performance of a minutiae extraction algorithm relies heavily on the quality of the thinning algorithm. So, a fast fingerprint thinning algorithm is proposed. The algorithm works directly on the gray-scale image as binarization of fingerprint causes many spurious minutiae and also removes many important…
Latent fingerprints are one of the most important and widely used sources of evidence in law enforcement and forensic agencies. Yet the performance of the state-of-the-art latent recognition systems is far from satisfactory, and they often require manual markups to boost the latent search performance. Further, the COTS systems are proprietary and do not output the true comparison scores between a latent and reference prints to conduct quantitative evidential analysis. We present an end-to-end latent fingerprint search system, including automated region of interest (ROI) cropping, latent image …
Escuela Politecnica Superior（高等理工学院）；Universidad Autonoma de Madrid（马德里自治大学）
The effect of image quality degradation on the verification performance of automatic fingerprint recognition is investigated. We study the performance of two fingerprint matchers based on minutiae and ridge information under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system for a number of different image quality criteria.
A B-Spline Function Based 3D Point Cloud Unwrapping Scheme for 3D Fingerprint Recognition and Identification
School of Systems and Computing, UNSW Canberra（UNSW Canberra 系统与计算学院）；Information Systems, University of Canberra（堪培拉大学信息系统系）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；School of Engineering & Technology, UNSW Canberra（UNSW Canberra 工程与技术学院）
Dublin City University（都柏林城市大学）；Universidad Autonoma de Madrid（马德里自治大学）；Halmstad University（哈尔姆斯塔德大学）
Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (…
University of Cagliari（卡利亚里大学）；Northwestern Polytechnical University（西北工业大学）
We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms. To the best of our knowledge, only a previous investigation was done with spoofs from latent prints. In this paper, we focus on using snapshot pictures of latent fingerprints. These pictures provide molds, that allows, after some digital processing, to fabricate high-quality spoofs. Taking a snapshot picture is much simpler than developing fingerprints left on a surface by mag…
Cambridge Institute of Technology（剑桥理工学院）；University Visvesvaraya College of Engineering（维斯瓦瓦拉亚工程大学学院）；Bangalore University（班加罗尔大学）；Seemanta Engineering College（西曼塔工程学院）；F.M. University（F.M.大学）
Forensic applications like criminal investigations, terrorist identification and National security issues require a strong fingerprint data base and efficient identification system. In this paper we propose DWT based Fingerprint Recognition using Non Minutiae (DWTFR) algorithm. Fingerprint image is decomposed into multi resolution sub bands of LL, LH, HL and HH by applying 3 level DWT. The Dominant local orientation angle θ and Coherence are computed on LL band only. The Centre Area Features and Edge Parameters are determined on each DWT level by considering all four sub bands. The comparison …
Enhancing Fingerprint Recognition Systems: Comparative Analysis of Biometric Authentication Algorithms and Techniques for Improved Accuracy and Reliability
Fingerprint recognition is often a game-changing step in establishing evidence against criminals. However, we are increasingly finding that criminals deliberately alter their fingerprints in a variety of ways to make it difficult for technicians and automatic sensors to recognize their fingerprints, making it tedious for investigators to establish strong evidence against them in a forensic procedure. In this sense, deep learning comes out as a prime candidate to assist in the recognition of damaged fingerprints. In particular, convolution algorithms. In this paper, we focus on the recognition …
Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only if the fingerprint image is of high resolution which necessitates a model that enhances the image quality of the conventional 500 ppi legacy fingerprints preserving the fine details. To find a solution for recovering pore information from …
High-resolution fingerprint recognition often relies on sophisticated matching algorithms based on hand-crafted keypoint descriptors, with pores being the most common keypoint choice. Our method is the opposite of the prevalent approach: we use instead a simple matching algorithm based on robust local pore descriptors that are learned from the data using a CNN. In order to train this CNN in a fully supervised manner, we describe how the automatic alignment of fingerprint images can be used to obtain the required training annotations, which are otherwise missing in all publicly available datase…
IIIT Hyderabad（印度海得拉巴国际信息技术学院）；Michigan State University（密歇根州立大学）；Sony Research India Pvt. Ltd.（索尼印度研究私人有限公司）；Sony Group Corporation（索尼集团公司）
Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this paper, we reformulate the workings of a typical fingerprint recognition system. In particular, we posit that both spoof detection and fingerprint recognition are correlated tasks. Therefore, rather than performing the two tasks separately, we propose a joint model for spoof detection and matching to simultaneously perform both tasks without compromising the accuracy of either task. We demonstrate the capability of our joint model to obtain …
The use of physical and behavioral characteristics for human identification is known as biometrics. Among the many biometrics traits available, the fingerprint is the most widely used. The fingerprint identification is based on the impression patterns, as the pattern of ridges and minutiae, characteristics of first and second levels respectively. The current identification systems use these two levels of fingerprint features due to the low cost of the sensors. However, due the recent advances in sensor technology, it is possible to use third level features present within the ridges, such as th…
Department of Computer Science and Engineering（计算机科学与工程系）；Michigan State University（密歇根州立大学）；East Lansing（东兰辛（城市名，非机构））；Michigan 48824（密歇根州 48824（邮编，非机构））
We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy. To compensate for the lack of sufficient number of minutiae in poor quality latent prints, we generate a set of virtual minutiae. However, due to a large number of these regularly placed virtual minutiae, texture based template matching has a large computational requirement compared to matching true minutiae templates. To improve both the accuracy and efficiency of the texture template matching, we investigate: i) both original and enhanced fingerprin…
Indian Institute of Technology Indore（印度理工学院印多尔分校）
Fingerprint recognition techniques are immensely dependent on quality of the fingerprint images. To improve the performance of recognition algorithm for poor quality images an efficient enhancement algorithm should be designed. Performance improvement of recognition algorithm will be more if enhancement process is adaptive to the fingerprint quality (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment algorithm clusters the fingerprint images in appropriate quality class of dry, wet, normal dry, norma…
New York University Tandon（纽约大学坦登）；Michigan State University（密歇根州立大学）
Recent research has demonstrated the vulnerability of fingerprint recognition systems to dictionary attacks based on MasterPrints. MasterPrints are real or synthetic fingerprints that can fortuitously match with a large number of fingerprints thereby undermining the security afforded by fingerprint systems. Previous work by Roy et al. generated synthetic MasterPrints at the feature-level. In this work we generate complete image-level MasterPrints known as DeepMasterPrints, whose attack accuracy is found to be much superior than that of previous methods. The proposed method, referred to as Late…
