21,969 research outputs found
Latent Fingerprint Recognition: Fusion of Local and Global Embeddings
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 a 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
Fingerprint Identification - New Directions
In most of the algorithms that have been suggested in this report, the fingerprint image is reduced to a relatively short sequence of integers. This reduces the memory size required by the database. Each algorithm is intended to exploit specific properties and features of the fingerprint that vary from finger to finger, and that can be localized relatively fast using digital techniques, thus also reducing the computational time requirements to a minimum. In each case, the sensitivity of the algorithm to small variations in the image was also discussed, with the aim of reducing the False Rejection Rate, and of increasing the general robustness of the algorithm
Using biometrics authentication via fingerprint recognition in e-Exams in e-Learning environment
E-learning is a great opportunity for modern life. Notably, however, the tool needs to be coupled with efficient and reliable security mechanisms to ensure the medium can be established as a dependable one. Authentication of e-exam takers is of prime importance so that exams are given by fair means. A new approach shall be proposed so as to ensure that no unauthorised individuals are permitted to give the exams
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