•A critical review on electrochemical development and imaging of latent fingerprints.•Electrochemical systems for fingerprint development and imaging are summarized.•Underlying fundamentals and ...potential applications of these methods are presented.•Limitations of existing methods and perspectives on future research are discussed.
Latent fingerprints containing morphological and biochemical information are one of the most important evidence existed at crime scenes that can be used for personal identification. Despite some traditional development and imaging methods, various novel methods based on different principles have been explored to visualize latent fingerprints in recent years, among which many interesting methods established by using electrochemical techniques have proved to be very efficient. Up to now, a number of electrochemical systems have been utilized to create or detect the disparity between fingerprint ridge area and background area, resulting in sensitive development and imaging of latent fingerprints. In this review, we highlight the recent progress in electrochemical development and imaging of latent fingerprints. To be specific, the introduction of fundamentals and applications of this research area is separated into four main sections: fingerprint development by electrochemical deposition, fingerprint imaging by electrochemiluminescence (ECL), fingerprint imaging by scanning probe electrochemistry techniques, and fingerprint analysis using other electrochemical methods. Finally, our perspectives on future research directions are also presented and discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
2.
PrintsGAN: Synthetic Fingerprint Generator Engelsma, Joshua James; Grosz, Steven; Jain, Anil K.
IEEE transactions on pattern analysis and machine intelligence,
05/2023, Volume:
45, Issue:
5
Journal Article
Peer reviewed
Open access
A major impediment to researchers working in the area of fingerprint recognition is the lack of publicly available, large-scale, fingerprint datasets. The publicly available datasets that do exist ...contain very few identities and impressions per finger. This limits research on a number of topics, including e.g., using deep networks to learn fixed length fingerprint embeddings. Therefore, we propose PrintsGAN, a synthetic fingerprint generator capable of generating unique fingerprints along with multiple impressions for a given fingerprint. Using PrintsGAN, we synthesize a database of 525k fingerprints (35K distinct fingers, each with 15 impressions). Next, we show the utility of the PrintsGAN generated dataset by training a deep network to extract a fixed-length embedding from a fingerprint. In particular, an embedding model trained on our synthetic fingerprints and fine-tuned on a small number of publicly available real fingerprints (25K prints from NIST SD 302) obtains a TAR of 87.03% @ FAR=0.01% on the NIST SD4 database (a boost from TAR=73.37% when only trained on NIST SD 302). Prevailing synthetic fingerprint generation methods do not enable such performance gains due to i) lack of realism or ii) inability to generate multiple impressions per finger. Our dataset is released to the public: https://biometrics.cse.msu.edu/Publications/Databases/MSU_PrintsGAN/ .
Optical coherence tomography (OCT) is a high-resolution imaging technology probing the internal structure of multilayered tissues. Since it provides subsurface fingerprint information that is ...identical to the surface texture but unaffected by any surface defects, OCT-based fingerprints open up a new domain for establishing robust and high-security automatic fingerprint identification systems (AFISs). However, the development of OCT-based fingerprint recognition is hindered by the lack of public OCT-based fingerprint database for algorithm analysis and testing. This article, for the first time, established an OCT-based fingerprint database with thousands of fingers using our custom-built acquisition device. The website of this data set is https://github.com/CV-SZU/ . Moreover, the images included in the database were selected after quality evaluation based on image resolution, image size, effective measured area, and the number of extractable features. Finally, case studies, including antispoofing, multiple subsurface fingerprint reconstruction, and fingerprint verification, were discussed based on the developed database. The database can serve as a benchmark for developing effective antispoofing, live detection, and high-accurate fingerprint recognition algorithms. It will significantly promote the research in the area of fingerprint biometric and will also advance the development of commercial products.
One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., ...latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local minutiae-based embeddings and have not directly leveraged global representations for matching. In this paper, we combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput. The combination of both local and global representations leads to improved recognition accuracy across NIST SD 27, NIST SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate, respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02, respectively) identification scenarios on a gallery of 100K rolled fingerprints. Not only do we fuse the complimentary representations, we also use the local features to guide the global representations to focus on discriminatory regions in two fingerprint images to be compared. This leads to a multi-stage matching paradigm in which subsets of the retrieved candidate lists for each probe image are passed to subsequent stages for further processing, resulting in a considerable reduction in latency (requiring just 0.068 ms per latent to rolled comparison on an AMD EPYC 7543 32-Core Processor, roughly 15K comparisons per second). Finally, we show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets.
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 method which estimates the spatial transformation between a pair of fingerprints through a dense fingerprint patch alignment and matching procedure. Given a pair of fingerprints to match, we bypass the minutiae extraction step and take uniformly sampled points as key points. Then the proposed patch alignment and matching algorithm compares all pairs of sampling points and produces their similarities along with alignment parameters. Finally, a set of consistent correspondences are found by spectral clustering. Extensive experiments on NIST27 database and MOLF database show that the proposed method achieves the state-of-the-art registration performance, especially under challenging conditions. Code is made publicly available at: https://github.com/Gus233/Latent-Fingerprint-Registration .
Prior to the large-scale deployment of any biometric system it is necessary to have a realistic estimation of its performance. In the domain of fingerprint biometrics, three-dimensional (3D) ...fingerprint scan technology has been developing very fast. However, there is no 3D fingerprint database publicly available for research purposes. To evaluate the matching performance of 3D fingerprints and the compatibility of 2D and 3D fingerprints comprehensively, a large fingerprint database using two commercial fingerprint sensors is established. The database consists of both 3D fingerprints and their corresponding 2D fingerprints. Several verification experiments using a commercial fingerprint identification software are carried out. The results serve as the performance criterion of the database, which will be released publicly together with the database in late 2014.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
7.
Automated Latent Fingerprint Recognition Cao, Kai; Jain, Anil K.
IEEE transactions on pattern analysis and machine intelligence,
04/2019, Volume:
41, Issue:
4
Journal Article
Peer reviewed
Open access
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 ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short candidate list from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7 percent for the NIST SD27 and 75.3 percent for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7 and 70.8 percent rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7 and 75.3 to 73.3 percent (74.4 percent) and 76.6 percent (78.4 percent) on NIST SD27 and WVU latent databases, respectively.
•An effective and simple multiple subsurface fingerprint reconstruction method.•A novel fusion strategy to obtain a robust subsurface fingerprint.•A robustness and high-security fingerprint ...recognition system.•OCT-based, 2D surface and artificial fingerprint databases.
Traditional fingerprint recognition systems are vulnerable to attacks, such as the use of artificial fingerprints, and poor performance will be achieved if the captured surface fingerprints are of low-quality. Developing high-security and robust fingerprint recognition systems is of increasing concern in modern society. The introduction of optical coherence tomography (OCT) for fingerprint imaging opens up a new research domain for fingerprint recognition due to its ability to capture the depth information of skin layers. This paper proposes a fingerprint recognition system based on OCT. The research first establishes a database with normal, worn-out, artificial and degraded fingerprints imaged by our custom-built, in-house OCT device. Then, we propose to reconstruct three layers of subsurface fingerprints by considering diverse skin layer information. For each of the subsurface fingerprints divided according to the physical structure of the fingertip, we propose a simple yet effective projection-based reconstruction method. Finally, a pixel-level fusion strategy based on the local image quality is proposed to the fuse the three levels of subsurface fingerprints for robust fingerprint recognition. In our experiments, we show that as much diverse fingerprint information can be retained as possible by the proposed subsurface fingerprint reconstruction method. We also demonstrate the effectiveness and efficiency of the proposed method by comparing it with existing state-of-the-art internal fingerprint reconstruction approaches. The robustness and high anti-spoofing ability of the proposed system is verified by comparing the matching performance evaluated on the established OCT-based database and another database with the same fingerprints imaged by a commercial optical sensor. The best EER and FMR100 evaluated on OCT-based fingerprints are 0.42% and 0.36%, respectively. The best EER and FMR100 evaluated on traditional 2D surface fingerprints are 8.05% and 18.18%, respectively, which shows the vast potential of the proposed system in current automated fingerprint recognition systems (AFRSs).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
With the increasing use of biometric identity authentication, biological key generation technology is receiving much attention. A high‐strength key that is easy to store and manage can be generated ...from biological characteristics, which can improve the convenience and security of user‐encryption operations. However, the generation of a high‐strength, stable, and robust key using the currently available fingerprint bio‐key generation technology is difficult. This paper proposes a three‐layer framework for fingerprint bio‐key generation that is composed of a fingerprint bio‐key preprocessor, fingerprint bio‐key stabilizer (FPBK_Stabilizer), and fingerprint bio‐key fuzzy extractor. In the FPBK_Stabilizer, feature selection and layer‐by‐layer convolution projection characteristics from deep neural networks are used to effectively eliminate the instability between fingerprint samples. Furthermore, a suitable multilayer convolutional projection fingerprint bio‐key generation model is designed for generating the fingerprint bio‐key. The results of a fingerprint bio‐key generation experiment involving a fingerprint library comprising 100 people verified the efficacy of the proposed framework. Specifically, the proposed framework exhibited a generation intensity
>1024 bits, accuracy rate
>98.0%, and misrecognition rate
<1.5%, thereby verifying its high‐strength, stable, and robust fingerprint bio‐key generation capability.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Fingerprints are the one of the most important means in the forensics as a means of identification of the criminals owning to the uniqueness and the distinct features in them. Fingerprint ...identification is considered as an important means for the identification of the people around the globe. Minutiae are the details present in the human fingerprints which are used as a means of identification and verification. Minutiae are the distinctive points which can be used for the effective reconstruction of the fingerprint image. However, there was a limitation that was considered. The minutiae points are completely not enough for reconstruction of the image. Many spurious minutiae are not included and the results for the latent fingerprints are not as accurate as they are for the normal data sets. In this paper, a novel technique has been proposed which considers the minutiae density and the orientation field direction for the reconstruction of the fingerprint. Two public domain databases Fingerprint verification competition 2002 (FVC2002) and fingerprint verification competition 2004 (FVC2004) have been used for the experimental results and to validate the suggested methods for the fingerprint reconstruction and enhancement.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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