Signature Verification using Static and Dynamic Features

A signature verification algorithm based on static and dynamic fea- tures of online signature data is presented. Texture and topological features are the static features of a signature image whereas the digital tablet captures in real-time the pressure values, breakpoints, and the time taken to create a signature

DS theory based fingerprint classifier fusion with update rule to minimize training time

This paper presents a novel fingerprint classifier fusion algorithm using Dempster-Shafer theory concomitant with update rule. The proposed algorithm accurately matches fingerprint evidences and also e#ciently adapts to dynamically evolving database size without compromising accuracy or speed. We experimentally validate our approach using three fingerprint recognition algorithms based on minutiae, ridges, and image pattern features. The performance of our proposed algorithm is compared with these individual fingerprint algorithms and commonly used fusion algorithms. In all cases, the proposed Dempster Shafer theory with update rule outperforms existing algorithms even with partial fingerprint image. We also show that as the database size increases, the proposed algorithm is designed to operate on only the augmented data instead of the entire database, thereby reducing the training time without compromising the verification accuracy

Robust Biometric Image Watermarking for Fingerprint and Face Template Protection

This paper presents a combined DWT and LSB based biometric watermarking algorithm that securely embeds a face template in a fingerprint image. The proposed algorithm is robust to geometric and frequency attacks and protects the integrity of both the face template and the fingerprint image. Experimental results performed on a database of 750 face and 750 fingerprint images show that the algorithm has the advantages of both the existing DWT and LSB based algorithms. A multimodal biometric algorithm is used as a metric to evaluate the combined performance of both face and fingerprint recognition