Performance Evaluation of Face Recognition Based on PCA, LDA, ICA and Hidden Markov Model

Abstract

This paper describes a face recognition methods based on Principle Component Analysis (PCA), Linear Discriminant Analysis and Independent Component Analysis and Hidden Markov Model. Face recognition is an important research problem spanning numerous fields and disciplines. Face recognition draws a complex task and the changes in incident illumination ,head pose, facial expression, size and other external factors. HMM based framework for face recognition, face detection and it requires a one dimensional observation sequence and images are two dimensional, the images should be converted into either 1D temporal sequences or 1D spatial sequences. The paper presents with various face recognition techniques used for solving the problem. Traditional techniques such as holistic methods (PCA,LDA,ICA), feature based methods(Elastic Bunch Graph Matching, Dynamic Link Matching),model based methods(Active Appearance Model,3D Morphable Models) and hybrid method(Markov Random Field Method) are well known for face detection and recognition.