Authentication and Security Biometrics System based on Face Recognition and Palm Recognition

In recent time biometric security systems is major requirement to avoid theft or spoof attacks in many places. So, multimodal biometrics technology has attracted substantial interest for its highest user acceptance, high security, high accuracy, low spoof attack and high recognition performance in biometric recognition system. This multimodal biometrics system introduces recognition of person from two things i.e. face & palm print. Principal Component Analysis (PCA) algorithm is used for reduction of dimension & extraction of features in terms of eigenvalues & eigenvectors. With the results of face & palm prints and output as per neural network classifier which gives the correct information about genuine or imposter identity. Automatic person identification is an important task in computer vision and related applications. The proposed work is an implementation of person identification fusing face, palm biometric modalities used PCA based neural network classifier for feature extraction from the face and palm images and hamming distance for calculating iris templates. These features fused and used for identification. Identification was made using Eigen faces, Eigen ears, Template of iris and their features tested over the self created image database.