Palmprint Authentication Using Pattern Classification Techniques

Biometric technology incorporates several physiological and behavioral traits for personal authentication whenever deployed for security systems. Palmprint is one of the physiological trait has been utilized several times for key applications. This paper proposes a pattern classification approach for palm print authentication which utilizes soft computing techniques to classify a claimed identity into its appropriate class. The presented approach operates on feature level classification using 2D Gabor filter for feature representation and Principal Component Analysis (PCA) for computing weights as features .These features are used to train the classifiers by taking each user as a separate class. K-Nearest Neighbor, (KNN) and Probabilistic Neural Network (PNN) based classifiers are utilized in classification. These classifiers are also employed for score level classification by computing the matching scores using normalized hamming distance. The experiments are carried out on HongKong PolyU database which has been a benchmark database for palmprint authentication. The proposed techniques operate on very low false acceptance rate (FAR) of .0011% and false rejection rate (FRR) of 3% which shows the reliability of the proposed work.