Directional Energy Based Feature Level Multimodal System Using Palm and Fingerprints

The ever increasing demand of security has resulted in wide use of Biometric systems. Despite overcoming the traditional verification problems, the unimodal systems suffer from various challenges like intra class variation, noise in the sensor data etc, affecting the system performance. These problems are effectively handled by multimodal systems. In this paper, we present a feature level fused multimodal approach using palm and finger prints. Directional energy based feature vectors of palm and fingerprint identifiers are combined to form joint feature vector that is subsequently used to identify the individual using a distance classifier. The proposed multimodal system is tested on a developed database consisting of 440 palm and finger prints each of 55 individuals. Receiver Operating Characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for multimodal system depicts improved performance compared to 2.822% and 2.553% of palm and finger prints identifiers respectively.