Discrete Lion Swarm Optimization Algorithm for face recognition

Abstract

In various applications, face recognition plays an important role such as identification of a person, biometrics via their Closed-Circuit Television (CCTV) cameras, identity cards and etc. Besides, various biometrics like palm print, fingerprint, iris, etc plays a significant role. Therefore, in this research, a face recognition technique is developed for the classification phase as well as feature extraction. Moreover, Discrete Lion Swarm Optimization Algorithm (DLSA) is developed Deep Belief Network (DBN) for face recognition. At first, in the database, the images experience feature extraction here the feature like m-Co-HOG, Kernel based Scale Invariant Feature Transform (K-SIFT) , besides with Active Appearance Models (AAM) features which are extracted from the image. Subsequently, developed DLSA-DBN is used for the classification. The simulation of the developed technique is performed by exploiting the CVL database. In addition, the developed model outperforms the conventional models for False Acceptance Rate (FAR), accuracy, and False Rejection Rate (FRR), correspondingly.

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