Face recognition via optimized features fusion

Abstract

Face recognition has received considerable attention in the field of computer vision and pattern recognition. The important applications of face recognition include but are not limited to Airport security, card security in ATM's, visa processing and the passport verification. Although there has been rigorous research in this area for almost a decade, the scientists have not been able to provide and agree to a standard for obtaining the salient information in facial images utilizing feature's categories. In this article, we have presented an approach where features are collected containing local and global face information (i.e. geometric and appearance-based features). These features are fused, resulting in an increase in the face-recognition accuracy. First, the global features are obtained by utilizing Discrete Cosine Transform and Local facial features via Local Binary Pattern. In the next stage, both local and global features are combined using the concatenation method resulting in an increase in features. To reduce the data dimensions, Particle Swarm Optimization (PSO) along with Genetic Algorithm (GA) is applied to eliminate the redundant features that provide the optimized feature sets. We also provide empirical results of our proposed system. The system has been evaluated using ORL and Labeled Faces in the Wild (LFW) face databases. We have been able to obtain a promising 98% accuracy rate by using PSO-GA based optimized features albeit the reduced number of features. Features' fusion enables proposed system to be robust to variations like facial expression change, illumination effects and occlusions.