Face recognition under varying expressions and illumination using particle swarm optimization

Social networks generate enormous amounts of visual data. Mining of such data in recommender systems is extremely important. User profiling is carried out in recommender systems to build the holistic persona of the user. Identification and grouping of images in these systems is carried out using face recognition. It is one of the most appropriate biometric features in such situations. Ever since the first use of face recognition in security and surveillance systems, researchers have developed many methods with improved accuracy. Face recognition under variant illumination is still an open issue and diverging facial expressions reduces the accuracy even further. State of the art methods produced an average accuracy of 90%.In this study, a computationally intelligent and efficient method based on particle swarm optimization (PSO) is developed. It utilizes the features extracted from texture and wavelet domain. Discrete Wavelet Transform provides the advantage of extracting relevant features and thereby reducing computational time and an increase in recognition accuracy rate. We apply particle swarm optimization technique to select informative wavelet sub-band. Furthermore, the proposed technique uses Discrete Fourier Transform to compensate the translational variance problem of the discrete wavelet transform. The proposed method has been tested on the CK, MMI and JAFFE databases. Experimental results are compared with existing techniques and the results indicate that the proposed technique is more robust to illumination and variation in expressions, average accuracy obtained over the CK, MMI and JAFFE datasets is 98.6%, 95.5%, and 98.8% respectively. (C) 2018 Elsevier B.V. All rights reserved.