Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Sadiq H. Abdulhussain, Basheera M. Mahmmod, Amer AlGhadhban and Jan Flusser
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Sadiq H. Abdulhussain: Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq
Basheera M. Mahmmod: Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq
Amer AlGhadhban: Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 682507, Saudi Arabia
Jan Flusser: Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou vìží 4, 18208 Prague, Czech Republic

Mathematics, 2022, vol. 10, issue 15, 1-28

Abstract: Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.

Keywords: face recognition; orthogonal polynomials; orthogonal moments; feature extraction; block processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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