Face Recognition for Single and Different Facial Expression

In this paper presents and analyses the performance of Principal Component Analysis (PCA) based technique for face recognition. The face is our main hub of thought in common intercourse, in concert a major part in assigning identity and feeling. We can distinguish thousands of faces learned throughout our lifetime and identity recognizable faces at a look even after years of division. This skill is give up healthy, regardless of large changes in the visual incentive due to presentation conditions, appearance, aging, and distraction such as spectacles, beards, changes in hairstyle. Through human faces are complex in shape, face recognitions is not difficult for a human brain whereas for a computer this job is not easy. We think recognition of human faces with two facial appearance: single and differential. The images that are captured previously constitute the training set. From these images eigenface 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. Currently I get result for the single facial expression now I am operational for dissimilar facial appearance.