Enhanced face recognition using euclidean distance classification and PCA

Biometrics is the study of methods for uniquely recognizing humans based upon one or more physical or behavioral traits such as; face, voice, fingerprint, gait, iris, signature and hand geometry. Several research works have been reported in literature, still there is a lack of accurate and robust methods and techniques. As compared with other biometrics systems using palm print, fingerprint and iris, Face recognition has other advantages because of its non-contact process. Face images of a person can be taken from a distance without touching the person who is to be identified, and the identification does not have need of interaction with the person. In the present work, the complete work is carried out in two phases viz. Face Detection and Face Recognition. In the first phase, the strategy applied to face detection from different sources like still images, webcams and videos. In the second phase, the strategy that is applied to the face recognition is Principal Component Analysis (PCA).