Title: Feature selection for face authentication systems: feature space reductionism and QPSO

Authors: Kamal Abdelraouf ElDahshan; Eman K. Elsayed; Ashraf Aboshosha; Ebeid Ali Ebeid

Addresses: Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt ' Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt ' NCRRT, Atomic Energy Authority, Nasr City, Cairo, Egypt ' Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt

Abstract: In face authentication systems, the feature selection (FS) process is very important because any feature extractor introduces some irrelevant or noisy features. These features can affect in the performance of such systems. In this paper, a new method is proposed to reduce the computations time in the facial feature selection. Quantum Fourier transforms (QFT), discrete wavelet transform (DWT). Discrete cosine transform (DCT) and scale invariant feature transform (SIFT) are employed separately as features' extractors. The proposed algorithm denoted by FSR_QPSO has two phases: feature space reductionism (FSR) and optimal feature selection based on quantum particle swarm optimisation (QPSO). FSR reduces the size of the feature matrix by selecting the best vectors (rows) and rejects the worst. Then QPSO is applied to fetch the optimal features set over the reduced space that contains the best vectors only. The proposed algorithm has been tested on ORL and Face94 databases. The experimental results show that the proposed algorithm reduces feature selection time against the case of using complete feature space.

Keywords: face-based authentication; feature selection; quantum Fourier transforms; QFT; discrete wavelet transform; DWT; discrete cosine transform; DCT; scale invariant feature transform; SIFT; feature space reductionism; FSR; quantum particle swarm optimisation; QPSO.

DOI: 10.1504/IJBM.2019.102861

International Journal of Biometrics, 2019 Vol.11 No.4, pp.328 - 341

Received: 08 Jun 2018
Accepted: 26 Mar 2019
Published online: 08 Oct 2019 *

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