Robust Face Recognition from Video based on Extensive Feature Set and Fuzzy_Bat Algorithm

Background/Objectives: This paper proposes a novel method of enhancing the face recognition process from video sequence with various pose and occlusion using an extensive feature set called Pose and Occlusion Invariant Feature set (POIF) and unsupervised learning technique. Methods/Statistical Analysis: Here an extensive feature set, POIF is created using local invariant feature namely Speeded Up Robust Feature (SURF), appearance features and weighted holo-entropy to find out the uniqueness of the face image. The Active Appearance Model (AAM) has been used to find the appearance based features in the face image. The proposed feature set, POIF is used to select the key frames in the video sequence and the key frame selection is optimised using unsupervised learning method namely, Fuzzy Clustering using Bat algorithm (FC-Bat). A dictionary of keyframes is then created, using which the faces from the test video is recognized. Findings: Experimental evaluation is done in MATLAB using McGill Real-World Unconstrained Face Video Database and Honda UCSD Dataset 1. The proposed system using FC_Bat algorithm is compared with Fuzzy optimized POIF feature set and Fuzzy c-means optimized POIF feature set and it is found that POIF with FC_Bat algorithm performs better with an accuracy of 97.5%. Applications/Improvements: The computational complexity of the proposed face recognition system is less as it uses unsupervised learning of features and best suits applications involving unlabelled data.