Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier

Abstract

The texture pattern produced by the finger knuckle bending is highly unique and makes the surface a distinctive biometric identifier. This paper presents literature survey and classification method for an emerging biometric identifier, namely Finger-Knuckle-Print (FKP), for personal identification. The FKP feature extraction is done using Principal Component Analysis (PCA) technique. Also Knuckle classification using nearest mean classifier is proposed in this paper. The experimental results from the proposed approach are promising and confirm the usefulness of this approach for personal identification. General Terms Principal Component Analysis (PCA), Region of Interest (ROI).