Fingerprint image quality evaluation algorithm based on visual perception model

doi:10.7523/j.ucas.2022.020

Abstract

Abstract: Fingerprint is known as the first material evidence in the field of criminal investigation and forensic science, and plays a very important role in combating crime and maintaining social stability. The conclusion of fingerprint identification is the key to the formation of effective evidence and the detection of criminal cases, and the quality of fingerprint image directly affects the accuracy and reliability of fingerprint identification conclusions, so accurate and objective fingerprint image quality evaluation algorithm is an indispensable tool to assist fingerprint experts in fingerprint identification. At present, the NFIQ2.0 fingerprint image quality evaluation algorithm developed by the National Institute of Standards and Technology (NIST) has attracted extensive attention from experts and scholars at home and abroad. However, the quality evaluation results of this algorithm deviate greatly from the quality evaluation results of fingerprint experts, and lack of quality evaluation for local areas of fingerprint images. Therefore, it can not meet the needs of criminal investigation and forensic science fingerprint identification tasks. In this paper, the problem of fingerprint image quality evaluation is extended to the quality space, and the quality evaluation algorithm based on expert perception is proposed by learning the quality perception strategy of fingerprint experts for the local area of ridges. The experimental results show that the overall quality evaluation results of the proposed algorithm are consistent with the quality evaluation results of fingerprint experts, and accord with the application scenarios of criminal investigation and forensic science. In addition, this paper further compared with the NFIQ2.0 algorithm on several international public fingerprint data sets, and the results show that the quality evaluation score of the proposed algorithm is more reasonable, and can effectively reduce false non-match rate (FNMR) of the fingerprint matching algorithm.

Key words: fingerprint image quality, fingerprint identification, quality spatial distribution, quality perception, NFIQ2.0

CLC Number:

TP391

FENG Qiliang, HAN Congying, ZHAO Tong. Fingerprint image quality evaluation algorithm based on visual perception model[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(6): 821-833.

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