Face Recognition and Gender Classification using Principal Component Analysis

Face recognition is one of the most challenging areas in the field of computer vision. In this thesis, a photometric (view based) approach is used for face recognition and gender classification. There exist several algorithms to extract features such as Principal Component Analysis (PCA), Fisher Linear Discriminate Analysis (FLDA), Image principal component analysis (IPCA), and various others. Principal component analysis is used for the dimensional reduction and for the feature extraction. Two face databases are taken in which one database contains the face images of male and one contains face images of females. On the basis of Euclidean Distance classification of the gender is done. Comparison between Euclidean Distance and Mahalanobis Distance for face recognition is also done with different number of test images. This method is tested on FERET and IIT KanpurŒs database