Title: Extraction and selection of binarised statistical image features for fingerprint recognition

Authors: Ahlem Adjimi; Abdenour Hacine-Gharbi; Philippe Ravier; Messaoud Mostefai

Addresses: LMSE Laboratory, University of Bordj Bou Arréridj, Elanasser, 34030 Bordj Bou Arréridj, Algeria ' LMSE Laboratory, University of Bordj Bou Arréridj, Elanasser, 34030 Bordj Bou Arréridj, Algeria ' PRISME Laboratory, University of Orléans, BP 6744 Orléans Cedex 2, France ' LMSE Laboratory, University of Bordj Bou Arréridj, Elanasser, 34030 Bordj Bou Arréridj, Algeria

Abstract: Due to their simplicity and efficiency, histogram-based descriptors are very used in the task of fingerprint recognition. In this work, we use a novel histogram based descriptor called binarised statistical image features (BSIF). The experiments have conducted on the standard FVC2002 database. We have extracted the BSIF histograms from sub-images around the core point of the fingerprint image and concatenated them to construct the final features vector. The experiments have shown that an increasing number of extracted sub-images lead to an increasing recognition rate, but lead also to higher dimension histogram which decreased performance of the system regarding computing time and memory capacity. To avoid this problem we have used a feature selection method based on the mutual information called interaction capping (ICAP) which selects the relevant bins of the BSIF histogram. The results showed that using feature selection method could reduce the dimensionality leading to a less computational complexity.

Keywords: biometrics; fingerprints; fingerprint identification; features extraction; binarised statistical image features; BSIF; local binary patterns; LBP; local phase quantisation; LPQ; texture descriptors; histogram; recognition rate; feature selection; mutual information; interaction capping; ICAP.

DOI: 10.1504/IJBM.2017.10005054

International Journal of Biometrics, 2017 Vol.9 No.1, pp.67 - 80

Received: 24 Jun 2016
Accepted: 10 Jan 2017

Published online: 13 May 2017 *

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