A comparative review of various approaches for feature extraction in face recognition

Face recognition is a type of biometric software application by using which, we can analyzing, identifying or verifying digital image of the person by using the feature of the face of the person that are unique characteristics of each person. These characteristics may be physical or behavioral. The physiological characteristics as like finger print, iris scan, or face etc and behavior characteristics as like hand-writing, voice, key stroke etc. Face recognition is very useful in many areas such as military, airports, universities, ATM, and banks etc, used for the security purposes. There are many techniques or algorithms that are used features extraction in face recognition. This paper make a review of some of those methods which are used for the face recognition that are Principal Component Analysis (PCA), Back Propagation Neural Networks (BPNN), Genetic Algorithm, and LDA, SVM, Independent Component Analysis(ICA). Each method has different -2 functions that are used for the face recognition. Dimensionality is reduced by using the Eigen face approach or PCA, LDA to extract the features from images. Genetic Algorithm is based on feature selection and Back propagation Neural Network (BPNN) is used for the classification of face images.