Title
Differential evolution based score level fusion for multi-modal biometric systems
Abstract
The purpose of a multimodal biometric system is to construct a robust classifier of genuine and imposter candidates by extracting useful information from several biometric sources which fail to perform well in identification or verification as individual biometric systems. Amongst different levels of information fusion, very few approaches exist in literature exploring score level fusion. In this paper, we propose a novel adaptive weight and exponent based function mapping the matching scores from different biometric sources into a single amalgamated matching score to be used by a classifier for further decision making. Differential Evolution (DE) has been employed to adjust these tunable parameters with the objective being the minimization of the overlapping area of the frequency distributions of genuine and imposter scores in the fused score space, which are estimated by Gaussian kernel density method to achieve higher level of accuracy. Experimental results show that, the proposed method outperforms the conventional score-level fusion rules (sum, product, tanh, exponential) when tested on two databases of 4 modalities (fingerprint, iris, left ear and right ear) of 200 and 516 users and thus confirms the effectiveness of score level fusion. The preliminary results provide adequate motivation towards future research in the line of the application of meta-heuristics in score level fusion.
Year
DOI
Venue
2014
10.1109/CIBIM.2014.7015441
CIBIM
DocType
Citations 
PageRank 
Conference
3
0.42
References 
Authors
25
6
Name
Order
Citations
PageRank
Satrajit Mukherjee130.42
Kunal Pal2363.42
Bodhisattwa Prasad Majumder341.10
Chiranjib Saha41119.57
Bijaya K. Panigrahi523529.75
Sanjoy Das622639.18