1,126 research outputs found
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
A Universal Latent Fingerprint Enhancer Using Transformers
Forensic science heavily relies on analyzing latent fingerprints, which are
crucial for criminal investigations. However, various challenges, such as
background noise, overlapping prints, and contamination, make the
identification process difficult. Moreover, limited access to real crime scene
and laboratory-generated databases hinders the development of efficient
recognition algorithms. This study aims to develop a fast method, which we call
ULPrint, to enhance various latent fingerprint types, including those obtained
from real crime scenes and laboratory-created samples, to boost fingerprint
recognition system performance. In closed-set identification accuracy
experiments, the enhanced image was able to improve the performance of the
MSU-AFIS from 61.56\% to 75.19\% in the NIST SD27 database, from 67.63\% to
77.02\% in the MSP Latent database, and from 46.90\% to 52.12\% in the NIST
SD302 database. Our contributions include (1) the development of a two-step
latent fingerprint enhancement method that combines Ridge Segmentation with
UNet and Mix Visual Transformer (MiT) SegFormer-B5 encoder architecture, (2)
the implementation of multiple dilated convolutions in the UNet architecture to
capture intricate, non-local patterns better and enhance ridge segmentation,
and (3) the guided blending of the predicted ridge mask with the latent
fingerprint. This novel approach, ULPrint, streamlines the enhancement process,
addressing challenges across diverse latent fingerprint types to improve
forensic investigations and criminal justice outcomes
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
DPD-DFF: a dual phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases
Nowadays, many companies and institutions need fast and reliable identification systems that are able to deal with very large databases. Fingerprints are among the most used biometric traits for identification. In the current literature there are fingerprint matching algorithms that are focused on efficiency, whilst others are based on accuracy. In this paper we propose a flexible dual phase identification method, called DPD-DFF, that combines two fingers and two matchers within a hybrid fusion scheme to obtain both fast and accurate results. Different alternatives are designed to find a trade-off between runtime and accuracy that can be further tuned with a single parameter. The experiments show that DPD-DFF obtains very competitive results in comparison with the state-of-the-art score fusion techniques, especially when dealing with large databases or impostor fingerprints
The fundamentals of unimodal palmprint authentication based on a biometric system: A review
Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases
Interpol review of fingermarks and other body impressions 2016–2019
This review paper covers the forensic-relevant literature in fingerprint and bodily impression sciences
from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The
review papers are also available at the Interpol website at: https://www.interpol.int/content/download/
14458/file/Interpol%20 Review%20 Papers%202019. pdf
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