Fingerprint matching, spoof and liveness detection: classification and literature review

doi:10.1007/s11704-020-9236-4

Front. Comput. Sci.

2021, Vol. 15

Issue (1) : 151310 https://doi.org/10.1007/s11704-020-9236-4

REVIEW ARTICLE

Fingerprint matching, spoof and liveness detection: classification and literature review

Syed Farooq ALI¹(

), Muhammad Aamir KHAN², Ahmed Sohail ASLAM³

¹. Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
². Department of Informatics and Systems, University of Management and Technology, Lahore 54770, Pakistan
³. Department of Computer Science, University of Management and Technology, Lahore 54770, Pakistan

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Abstract

Fingerprint matching, spoof mitigation and liveness detection are the trendiest biometric techniques, mostly because of their stability through life, uniqueness and their least risk of invasion. In recent decade, several techniques are presented to address these challenges over well-known data-sets. This study provides a comprehensive review on the fingerprint algorithms and techniques which have been published in the last few decades. It divides the research on fingerprint into nine different approaches including feature based, fuzzy logic, holistic, image enhancement, latent, conventional machine learning, deep learning, template matching and miscellaneous techniques. Among these, deep learning approach has outperformed other approaches and gained significant attention for future research. By reviewing fingerprint literature, it is historically divided into four eras based on 106 referred papers and their cumulative citations.

Keywords computer society template matching fingerprint recognition survey deep learning machine learning

Corresponding Author(s): Syed Farooq ALI

Just Accepted Date: 09 March 2020 Issue Date: 10 October 2020

Cite this article:

Syed Farooq ALI,Muhammad Aamir KHAN,Ahmed Sohail ASLAM. Fingerprint matching, spoof and liveness detection: classification and literature review[J]. Front. Comput. Sci., 2021, 15(1): 151310.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-9236-4
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I1/151310

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