A Systematic Review of PCA and Its Different Form for Face Recognition

Face recognition is an important part of our daily life. Face recognition is used either for verification (one-to-one matching) or for identification (one-to-many). Mainly face recognition consists of two categories feature based and appearance based. Feature-based method first process the input image to identify and measure distinctive facial features such as the eyes, mouth, nose, etc., as well as other fiducial marks and then compute the geometric relationships among those facial points, thus reducing the input facial image to a vector of geometric features. Appearance based method used holistic features of 2D image attempt to identify faces using global representations, i.e., descriptions based on the entire image rather than on local features of the face. In this paper holistic method is discussed using Principle Component Analysis. This paper presents a systematic review of different forms of Principle Component Analysis (PCA) for face recognition. Based on the brief review of different forms of PCA, comparison table of recognition rate for ORL and FERET database are prepared.