Abstract
A biometric identification system is an automatic pattern recognition system that identifies a person through their specific physiological and/or behavioral characteristics. Unimodal biometric system often suffers from some limitations due to noise in sensed data, intra-class variation, inter-class similarities, spoof attacks, etc. Multi-biometric systems seek to overcome some of these limitations by providing multiple pieces of evidence about the person identity. The increased performance of multi-biometric systems motivated our investigation of a new approach for multi-biometric identification systems based on the combination of five finger surface instances of a person. The proposed approach has the particularity of using a new fusion scheme based on the rank level integration method to consolidate the results obtained from the different five biometric instances. To the best of our knowledge, this is the first time the rank level fusion approach is designed to incorporate the results of five finger surface instances to produce more reliable recognition results. To highlight the contributions of our approach, this paper presents a comparative experimental study of several rank level fusion approaches that can be useful in combining multi-biometric systems. The experimental evaluation was conducted on the real hand database 'Sfax-Miracl hand database'. The experimental results suggest that our novel approach produces a significant performance improvement in the recognition accuracy over approaches based on individual finger surface. They also indicate that the rank level integration of the five finger surfaces is poised to provide a promising direction to finger surface-based multi-biometric systems.