Shape invariant recognition of segmented human face images using eigenfaces

This paper describes an efficient approach for face recognition as a two step process: (1) segmenting the face region from an image by using an appearance based model, (2) using eigenfaces for person identification for segmented face region. The efficiency lies not only in generation of appearance models which uses the explicit approach for shape and texture but also the combined use of the aforementioned techniques. The result is an algorithm that is robust against facial expressions variances. Moreover it reduces the amount of texture up to 12% of the image texture instead of considering whole face image. Experiments have been performed on Cohn Kanade facial database using ten subjects for training and seven for testing purposes. This achieved a successful face recognition rate up to 92.85% with and without facial expressions. Face recognition using principal component analysis (PCA) is fast and efficient to use, while the extracted appearance model can be further used for facial recognition and tracking under lighting and pose variations. This combination is simple to model and apply in real time.