Hand based multibiometric authentication using local feature extraction

Biometrics has wide applications in the fields of security and privacy. Since unimodal biometrics are subjected to various problems regarding recognition and security, multimodal biometrics have been used extensively nowadays for personal authentication. In this paper we have proposed an efficient personal identification system using two biometric identifiers, palm print and Inner knuckle print. In the recent years, palm prints and knuckle prints have overruled other biometric identifiers because of their unique, stable and novelty feature. The proposed feature extraction method for palm print is Monogenic Binary Coding (MBC), which is an efficient approach for extracting palm print features. Then for inner knuckle print recognition we have tried two algorithms named Ridgelet Transform and Scale Invariant Feature Transform (SIFT). Also we have compared their results in terms of recognition rate. We then adopt Support Vector Machine (SVM) for classifying the extracted feature vectors. Combining both knuckle print and palm print for personal identification will give better security and accuracy.