Multi-Model Biometrics Authentication System

Multi-model Biometrics authentication system have mainly two authentication process, face identification and signature verification. Facial expressions are a valuable source of information that accompanies facial biometrics. Early detection of physiological and psycho-emotional data from facial expressions is linked to the situational awareness module of any advanced biometric system for personal state re/identification. A set of Fisher scores is calculated through partial derivative analysis of the parameters estimated in each HMM. These Fisher scores are further combined with some traditional features such as log-likelihood, 2-D DCT and appearance based features to form feature vectors that exploit the strengths of both local and holistic features of human face. Neural Network is then applied to analyze these feature vectors for face recognition. Experimental results on a public available face database are provided to demonstrate the viability of this scheme. The performance of this system has been validated on three public databases. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. Handwritten signatures are considered as the most natural method of authenticating a person’s identity. The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification. A verification stage includes applying the extracted features of test signature to a trained neural network which will classify it as a genuine or forged. In this paper, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on parameters extracted from the signature using various image processing techniques. The Off-line Signature Recognition and Verification is implemented using MATLAB. This work has been tested and found suitable for its