Face Recognition and Hand Gesture Analysis System

An increasingly digital world, reliable personal authentication has become an important human Computer interface activity. National security, e- commerce and have access to a computer network. It is important to establish a person's identity, where some examples. Existing security measures depends on Based knowledge such as passwords, swipe cards or token -based approach and attitude to control access to physical and virtual spaces passport. Universal, such as methods, although very secure. Such as tokens, badges and access cards can be shared or stolen. Passwords and PIN Numbers can be stolen electronically. In addition, they cannot distinguish between authentic have access to or knowledge of the user and tokens. To make a system more secure and simple with the use of biometric authentication system such as face and hand gesture recognition for personal authentication. In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed, having approximately 99% of recognition rate. With using quantized Singular Values Decomposition (SVD) coefficients as features describing blocks of face images and also hand gesture analysis system available using skin segmentation.