Multimodal Biometrics using Feature Fusion

Abstract

Problem statement: Biometric is a unique, measurable physiological or behavioral characteristic of a person and finds extensive applications in authentication and authorization. Fingerprint, palm print, iris, voice, are some of the most widely used biometric for personal identification. To reduce the error rates and enhance the usability of biometric system, multimodal biometric systems are used where more than one biometric characteristic are used. Approach: In this study it is proposed to investigate the performance of multimodal biometrics using palm print and fingerprint. Features are extracted using Discrete Cosine Transform (DCT) and attributes selected using Information Gain (IG). Results and Conclusion: The proposed technique shows an average improvement of 8.52% compared to using palmprint technique alone. The processing time does not increase for verification compared to palm print techniques.