PCA Based Face Recognition and Facial Expression Identification System

The face is our primary focus of attention in social intercourse, playing a major role in conveying identity and emotion. We can recognize thousands of faces learned throughout our lifetime and identify familiar faces at a glance even after years of separation. This skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses, beards, changes in hairstyle. Though human faces are complex in shape, face recognition is not difficult for a human brain whereas for a computer this job is not easy. In this paper presents and analyzes the performance of Principle Component Analysis (PCA) based technique for face recognition. We consider recognition of human faces with two facial expressions: single and differential. The images that are captured previously constitute the training set. From these images Eigen faces are calculated. The image that is going to be recognized through our system is mapped to the same Eigen spaces. Next I used classification technique namely distance based used to classify the images as recognized or non-recognized. Presently I got result for the single facial expression now I am working for different facial