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

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

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

Deep Learning for the Analysis of Latent Fingerprint Images

Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. The accuracy of latent fingerprint identification by latent fingerprint forensic examiners has been the subject of increased study, scrutiny, and commentary in the legal system and the forensic science literature. Errors in latent fingerprint matchingcan be devastating, resulting in missed opportunities to apprehend criminals or wrongful convictions of innocent people. Latent fingerprint comparison is increasingly relied upon by law enforcement to solve crime, and prosecute offenders. The increasing use of this service places new strains on the limited resources of the forensic science delivery system. Currently, latent examiners manually mark the region of interest (ROI) in latent fingerprints and use features manually identified in the ROI tosearch large databases of reference full fingerprints to identify a small number of potential matches for subsequent manual examination. Given the large size of law enforcement databases containing rolled and plain fingerprints, it is very desirable to perform latent fingerprint processing in a fully automated way.This dissertation proposes deep learning models and algorithms developed in the context of machine learning for automatic latent fingerprint image quality assessment, quality improvement, segmentation and matching. We also propose techniques that help speed-up convergence of a deep neural network and achieve a better estimation of the relation between a latent fingerprint image patch and its target class. A unified frequency domain based framework for latent fingerprint matching using image patches, as well as a novel latent fingerprint super-resolution model that uses a graph-total variation energy of latent fingerprints as a non-local regularizer for learning optimal weights for high quality image reconstruction, are also proposed. Using the deep learning models, we aim at providing an end-to-end automatic system that solves the problems inherent in latent fingerprint quality assessment, quality improvement, segmentation and matching