A Novel Approach to Improve Biometric Recognition Using Rank Level Fusion
This paper proposes a novel approach for rank level fusion which gives improved performance gain verified by experimental results. In the absence of ranked features and instead of using the entire template, we propose using K partitions of the template. The approach proposed in the paper is useful for generating sequential ranks and survivor lists on partitions of template to boost confidence levels by incorporating information from partitions. The proposed algorithm iteratively generates ranks for each partition of the user template. Ranks from template partitions are consolidated to estimate the fusion rank for the classification. This paper investigates rank level fusion for palmprint biometric using two approaches: (1) fixed threshold and resulting survivor list, and (2) iterative thresholds and iteratively refined survivor list. The above approaches achieve similar performances as related manifestations of fusion architecture. The experimental results support the proposition of high in-template similarity of palmprint for a user and its relevance to the intra-modal fusion framework. Experimental results using proposed approach on real palmprint data from 100 users show superior performance with recognition accuracy of 99 % as compared to recognition accuracy of 95% achieved with the conventional approach.