Spider Monkey Inspired Optimization Algorithm Based Clustering For Face Recognition

Abstract

Face recognition is a fast-growing technology that is widely used in forensics such as criminal identification, secure access and prison security.It contrasts from other classification issues in that there are normally a more number of classes in face recognition. Thus, the validation accuracy of the conventional subsystem face recognition is unsuitable. In order to overcome those problems the suggested method utilizes the spider monkey inspired optimization (SMO) algorithm for face clustering and recognition. At first feature reduction is done by advance principal component analysis (APCA) which is a hybridization of PCA and linear discriminant analysis (LDA). After reducing the features, the selected features are grouped and faces are recognized by means of SMO algorithm. From the food search behavior of spider monkeys, SMO algorithm is developed. The proposed performance is evaluated by means of sensitivity, specificity and accuracy. Here the proposed method accomplishes 98.66% accuracy. Using the MATLAB platform, the recommended technique is implemented.