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Title: Robust perceptual fingerprint image hashing: a comparative study

Authors: Wafa Birouk; Atidel Lahoulou; Ali Melit; Ahmed Bouridane

Addresses: Computer Science Department, Faculty of Exact Sciences, University of Bejaia, 06000, Bejaia, Algeria ' Computer Science Department, University Mohammed Seddik Benyahia, 18000, Jijel, Algeria ' Computer Science Department, University Mohammed Seddik Benyahia, 18000, Jijel, Algeria ' Cybersecurity and Data Analytics Research Center, University of Sharjah, Sharjah, UAE

Abstract: This paper presents a robust perceptual hashing scheme for biometric template protection where the input fingerprint image is mapped into a sequence of Boolean values. Our aim is to develop a method that relies on the use of four functions namely SIFT, Harris, DWT and SVD. After extracting the minutiae, the scale-invariant feature transform (SIFT) is applied in order to extract the robust features against geometric attacks. The resulting vector is then filtered using Harris criterion to maintain only the stable key-points. Next, the fingerprint template is produced by image binarisation and decomposed into blocks. The hash code is finally obtained by concatenating the singular values computed on the approximation coefficients of each image block. Similarity between hash codes is evaluated by the normalised Hamming distance (HD). Comparative analysis to three similar methods indicates that the proposed hashing scheme shows better performances in terms of discriminative capability as well as robustness against acceptable image manipulations, such as JPEG compression, gamma correction, speckle noise, Gaussian blur, shearing and slight rotation.

Keywords: fingerprint image; perceptual hashing; minutiae extraction; scale-invariant feature transform; SIFT; Harris; singular value decomposition; SVD; discrete wavelet transform; DWT; acceptable attacks.

DOI: 10.1504/IJBM.2023.127724

International Journal of Biometrics, 2023 Vol.15 No.1, pp.59 - 77

Received: 03 Dec 2020
Accepted: 14 Sep 2021

Published online: 15 Dec 2022 *

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