PALMPRINT, FINGER KNUCKLE PRINT AND FACE FEATURES FOR THE HUMAN RECOGNITION SYSTEM

Abstract

In the real time application, the majority of the biometric systems are unimodal. The unimodal takes only one source of information like palmprint, face etc. for the person recognition. Some problems occurred by this unimodal are spoof attacks and intra-class variations. Due to the presence of multiple independent portions of data, multimodal biometrics prevails over these problems by fusion of two or more unimodal biometric systems. In this paper, a multimodal biometric system by integrating Palmprint, Finger Knuckle Print (FKP) and Face at the matching score level is proposed. The features are extracted using the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Features (SURF) and classified by K nearest neighbour (KNN) and Support Vector Machine (SVM). The experimental result for PolyU database shows the effectiveness of multimodal biometric system with reference to False Accept Rate (FAR), False Reject Rate (FRR) and Genuine Accept Rate (GAR).