Tetrolet Local Directional Pattern and Optimization-driven 2D-HMM for Face Recognition

Face recognition has achieved more attention in computer vision with the focus on modelling the expression variations of human. However use of a computer system is a challenging task, due to variation in expressions, poses, and lighting conditions. This paper proposes a face recognition system based on Tetrolet, Local Directional Pattern (LDP) and Cat Swam Optimization (CSO). Initially, the input image is pre-processed, where the region of interest is extracted using the filtering method. Then pre-processed image is given to the proposed descriptor, namely Tetrolet-LDP to extract the features of the image. The features are subjected to classification using the proposed classification module, called Cat Swarm Optimization-based 2-Dimensional Hidden Markov Model (CSO-based 2D-HMM) in which the CSO trains the 2D-HMM. The performance is analysed using the metrics, such as accuracy, False Rejection Rate (FRR), & False Acceptance Rate (FAR) and the system achieves high accuracy of 99.45%, and less FRR and FAR of 0.0035 and 0.0025.