Paper Details

Nowadays security becomes a most important issue regarding a spoof attack. So, multimodal biometricstechnology has attracted substantial interest for its highest user acceptance, high security, high accuracy, low spoof attackand high recognition performance in biometric recognition system. This multimodal biometrics system introducesrecognition of person from two things i.e. face & palm print. Principal Component Analysis (PCA) algorithm is used forreduction of dimension & extraction of features in terms of eigenvalues & eigenvectors. Feature level fusion techniqueused to fuse the results of face & palm prints and then gives the output as per neural network classifier which gives thecorrect information about genuine or imposter identity. Automatic person identification is an important task in computervision and related applications. Multimodal biometrics involves more than two modalities. The proposed work is animplementation of person identification fusing face, palm biometric modalities used PCA based neural network classifierfor feature extraction from the face and palm images and hamming distance for calculating iris templates. These featuresfused and used for identification. Better result was obtained if the modalities were combined. Identification was madeusing Eigen faces, Eigen ears, Template of iris and their features tested over the self created image database.

Ramanuj Gupta, Avinash Trivedil, Rakesh Koringa, Hardik Prajapati, “AUTHENTICATION AND SECURITY BIOMETRICS BASED ON FACE RECOGNITION”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 13, pp. -, January 2017.