Grey Wolf Optimizer with Linear Collaborative Discriminant Regression Classification based Face Recognition

Abstract

For the past few decades, biometric Face Recognition (FR) is the active research area in different domains such as image processing, pattern recognition, etc. The existing FR system has several limitations such as Single Sample Problem, maximum Reconstruction Errors (REs), these problems decreases the FR rate. In this research paper, an efficient FR method is proposed, namely Grey Wolf Optimizer based Linear Collaborative Discriminant Regression Classification (GWO-LCDRC). The optimization technique of GWO algorithm is applied in LCDRC to select the relevant weight value in LCDRC. For every training sample, optimal weight values are selected to improve the recognition rate. The proposed GWO-LCDRC method maximize the collaboration of Between Class RE (BCRE) and minimize the Within Class RE (WCRE). An experimental analysis conducted on the two standard facial databases namely ORL and YALE. The proposed GWO-LCDRC method improved the performance of recognition accuracy with respect to different training samples. Also, the performance is compared with the traditional methods Linear Regression Classification (LRC), Linear Discriminant Regression Classification (LDRC), and LCDRC. The overall experiment demonstrated that the proposed GWO-LCDRC method achieved approximately 3% and 6.5% of FR accuracy improvement with respect to ORL and YALE respectively.

Dense Feature Based Face Recognition from Surveillance Video using Convolutional Neural Network

“…These drawbacks reduce the Face Recognition rate. The Grey Wolf Optimizer based on Linear Collaboration discriminant Regression classifier is introduced in paper [6]. Here, recognition rate is improved by applying recognition technique of Grey Wolf Optimizer algorithm to Linear Collaboration discriminant Regression classifier.…”