A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

Latent fingerprint identification is attracting increasing interest because of its important role in law enforcement. Although the use of various fingerprint features might be required for successful latent fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform other methods. However, as many fingerprint feature representations exist, we sought to determine if the selection of feature representation has an impact on the performance of automated fingerprint identification systems. In this paper, we review the most prominent fingerprint feature representations reported in the literature, identify trends in fingerprint feature representation, and observe that representations designed for verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of the most popular fingerprint feature representations over a common latent fingerprint database. Therefore, we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than 10% for Rank-1 and 24% for Rank-100 comparing the minutiae in the database NIST SD27, illustrating the need of new minutia descriptors for latent fingerprint identification.This work was supported in part by the National Council of Science and Technology of Mexico (CONACYT) under Grant PN-720 and Grant 63894

IMPROVING THE RIGOR OF THE LATENT PRINT EXAMINATION PROCESS

This PhD thesis is a synthesis of a portfolio of interrelated previously published work that was conducted to improve the rigor, standardization, transparency, and quantifiability of the latent print examination process. The core of the work relates to the development, adoption, and implications of the Extended Feature Set (EFS). EFS is a formal international standard (incorporated in ANSI/NIST-ITL) that defines a method of characterizing the information content of friction ridge impressions — allowing latent print examiners to unambiguously document the bases of their determinations during examination. EFS is the enabling technology that has made all of the other elements of this portfolio of work possible: evaluations of the accuracy and reliability of latent print examiners’ determinations, evaluations of the reliability of examiners’ feature markup, evaluations of examiners’ assessments of sufficiency, evaluations of latent print quality, development of quality and distortion metrics, evaluations of AFIS accuracy, and the development of training materials to assist in improving the uniformity of examiners’ annotations of the features and attributes of friction ridge impressions. The thesis summarizes these previous publications, as well as discussing their implications and possible future research and tools that could leverage this body of work. -- Cette recherche doctorale présente la synthèse d’un portfolio de travaux et de publications ayant pour objectif d’améliorer la rigueur, la standardisation, la transparence et la quantification dans le cadre du processus d’identification des traces papillaires. L’élément fondateur de cette recherche est le développement, l’adoption et les implications du Extended Feature Set (EFS). EFS est un standard formel international (incorporé dans ANSI/NIST-ITL) qui définit la méthode de description des caractéristiques présentes sur les impressions papillaires. Il permet aux experts en lophoscopie de documenter de manière non-ambiguë les observations qui sont à la base des conclusions formulées à la suite des examens. EFS a été le facilitateur qui a rendu possible tous les autres éléments de ce portfolio de recherches, à savoir : l’évaluation de la fiabilité et l’exactitude des conclusions des experts en matière de traces papillaires, l’évaluation de la fidélité des annotations des experts, le développement de mesures de qualité et de la distorsion des traces, l’évaluation de l’exactitude des systèmes AFIS et finalement le développement d’une formation visant à améliorer la reproductibilité, entre experts, des annotations des caractéristiques papillaires et de leurs attributs. Cette recherche doctorale présente une synthèse de l’ensemble de ces travaux publiés et discute des implications de ceux-ci, des voies de recherche future ainsi que les outils qui pourraient y être associés

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research

Reconocimiento de huellas dactilares para aplicaciones forenses

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: mayo de 2015The author was awarded with a European Commission Marie Curie Fellowship under the Innovative Training Networks (ITN) in the project Bayesian Biometrics for Forensics (BBfor2, FP7-PEOPLE-ITN-2008) under Grant Agreement number 238803 between 2011 and 2013. The author was also funded through the European Union Project - Biometrics Evaluation and Testing (BEAT) for 2014 and 2015 which supported the research summarized in this Dissertatio

Asynchronous processing for latent fingerprint identification on heterogeneous CPU-GPU systems

Latent fingerprint identification is one of the most essential identification procedures in criminal investigations. Addressing this task is challenging as (i) it requires analyzing massive databases in reasonable periods and (ii) it is commonly solved by combining different methods with very complex data-dependencies, which make fully exploiting heterogeneous CPU-GPU systems very complex. Most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time. Indeed, the most accurate approach was designed for one single thread. This work introduces the fastest methodology for latent fingerprint identification maintaining high accuracy called Asynchronous processing for Latent Fingerprint Identification (ALFI). ALFI fully exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism for analyzing massive databases. Our approach reduces idle times in processing and exploits the inherent parallelism of comparing latent fingerprints to fingerprint impressions. We analyzed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results reveal that ALFI is in average 22x faster than the state-of-the-art algorithm, reaching a value of 44.7x for the best-studied case

Prevalence of Pores in Latent Fingerprints

Of the many biometric traits recognized today, fingerprints are the most prevalent and familiar. The analysis of fingerprints involves level 1, level 2, and/or level 3 detail in the identification of a potential match. Traditionally, fingerprint matching was completely performed by hand, utilizing the ACE-V method. Thanks to the development of rapidly evolving technology, fingerprint matching has become an automated procedure through the use of fingerprint matching algorithms. In the literature, there has been an increase in the interest of developing Automatic Fingerprint Identification System (AFIS) algorithms that include level 3 details in the matching process. These studies have utilized live scanned and/or inked fingerprints, rather than latent fingerprints. However, practical use of AFIS algorithms involves unknown fingerprints, such as those collected at crime scenes, which are often latent in nature. In addition, research has also found that there is a wide variety in size and shape of pore structure, making automatic detection of pores difficult. The resultant quality of latent fingerprints is subject to various factors at the time of deposition, such as the deposition surface, environmental conditions, and composition of the fingerprint itself. Consequently, these factors, in addition to the inherent variance in pore structure, may very well affect the observance and use of level 3 details within a fingerprint. If the prevalence of pores proves to be unreliable and inconsistent in latent fingerprints, the push for including level 3 detail in the AFIS matching process may all be for nothing. For this reason, the effects of latent fingerprint deposition factors on pore identification needs to be considered and currently appears to be greatly under studied. In effort to begin to fill this gap in the current research, newly deposited latent fingerprints were collected and developed using both black fingerprint powder and cyanoacrylate fuming. Developed fingerprints were subsequently imaged via digital scan or digital camera, and enhanced using either Image J or Adobe\textsuperscript{\textregistered} Photoshop\textsuperscript{\textregistered}. Following image enhancement, pores were manually identified and marked using the Federal Bureau of Investigation (FBI) developed Universal Latent Workstation (ULW) software. Qualitative assessment of the 633 fingerprints collected resulted in 380 usable fingerprints for the remainder of the study. Observations regarding pore count within the replicate fingerprint sets indicated that total pore count/presence was not consistent. The Mann Whitney U test indicated that neither development method, black fingerprint powder nor cyanoacrylate fuming, produced pore data any better or worse than the other. Lastly, assessment of pore location resulted in a greater number of similarity scores being lower than the established threshold, indicating that pore location is not as easily assessed nor interpreted as hoped