Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine Ridge Structure Dictionary

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kai CaoAnil K Jain

Abstract

Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate ("lights-out" capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving "lights-out" latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these...Continue Reading

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Citations

Dec 24, 2015·TheScientificWorldJournal·S Kanchana, G Balakrishnan
May 14, 2016·PloS One·Duy Hoang ThaiCarsten Gottschlich
Mar 13, 2020·Forensic Science International : Synergy·Nancy SinglaSanjeev Sofat
Dec 30, 2020·BMC Bioinformatics·Yanglin TuZhe Zhang
Jan 28, 2021·Frontiers in Robotics and AI·Uttam U DeshpandeSushma V Chaugule
Aug 12, 2019·Entropy·Ngoc Tuyen LeChih-Chiang Wang

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