Title: An optimised local feature compression using statistical and structural approach for face recognition

Authors: A. Divya; K.B. Raja; K.R. Venugopal

Addresses: Department of Electronics and Communication Engineering, University Visvesvaraya College of Engineering, Bangalore University, K.R. Circle, Bengaluru, Karnataka, India ' Department of Electronics and Communication Engineering, University Visvesvaraya College of Engineering, Bangalore University, K.R. Circle, Bengaluru, Karnataka, India ' Department of Electronics and Communication Engineering, University Visvesvaraya College of Engineering, Bangalore University, K.R. Circle, Bengaluru, Karnataka, India

Abstract: Face recognition is the current extensive research region studied among several recognition tasks in the field of pattern recognition. Face images captured under an unrestricted environment generally contain discrepancies in the pose, illumination and expression (PIE). To improve the robustness of the face image due to PIE variations, an optimised local feature compression (OLFC) is proposed using the matching algorithm and classifier. The pixel values of the images are structured as low picture element values (LPEV) and high picture element values (HPEV). The discrete wavelet transform and statistical methods are applied on LPEV and HPEV respectively to obtain substantial data and statistical features, which results in reduced features dimensions. Experiment is performed on six popular face databases (ORL, YALE, JAFFE, EYB, Faces-94 and FERET), illustrates an excellent performance with high recognition accuracy of 95.5%, 99.33%, 100%, 99.69%, 99.86% and 96.39% respectively with reduced error and computation time compared with existing methods.

Keywords: face recognition; discrete wavelet transform; DWT; Euclidean distance; artificial neural networks; ANNs.

DOI: 10.1504/IJCVR.2023.133134

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.5, pp.469 - 496

Received: 25 Aug 2021
Accepted: 10 Apr 2022
Published online: 01 Sep 2023 *

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