User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

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. User psychology is accounted has been taken into consideration which highly effects its eventual user acceptability and collectability among masses. Desirable image quality is achieved by a stable and user friendly image acquisition platform. In this paper, we present multimodal approach for palm and fingerprints by feature level and score level fusions (sum and product rules). The proposed multimodal systems are tested on a developed database consisting of 440 palm and fingerprints each of 55 individuals. In feature level fusion, 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. In score level fusion, the matching scores of individual classifiers are fused by sum and product rules. Receiver operating characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for feature level fusion shows best performance compared to score level fusion of 0.6141 and 0.5482% of sum and product rules, respectively. Multimodal systems, however, significantly outperform unimodal palm and fingerprints identifiers with EER of 2.822 and 2.553%, respectively.