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.

Optimized multi‐biometric enhancement analysis

“…In addition, comparative studies have shown that fusion at the score level outperforms other levels [15]. The proposition of [19] discusses the use of differential evolution, which has a similar structure to GA, in parameters tuning to reduce the overlapping of genuine and impostor distribution. Also, authors in [20] use DE to find the confidence factors of the belief assignments that reduce the weighted error rate.…”

“…On one hand, the authors in [17] propose an innovative scheme to fuse scores by combining the weighted mean and quasi-arithmetic mean without any learning process, while [18] uses consecutively a suite of derivatives on training data to estimate the vector score integration adapted to the multiclass problem that minimizes the probability of error. On the other hand, many evolutionary methods are applied to optimize these metrics with a learning process like differential evolution [19,20], particle swarm optimization (SO) [21] and quasi-convex optimization [22]. SO is suitable for different optimization applications.…”

“…Here, we propose to combine GP with the proposed CSO algorithm to generate different weighted fusion functions represented in a tree of arithmetic rules. Then, we compare our proposal to the fusion strategies of main proposals in the literature such as the use of norms [5], DE, PSO and BSA [19][20][21]25]. Our first version and application of GP is proposed in [26] whose extended experiments of the best trees are conducted on the Biosecure score database [27].…”

Score‐level fusion using generalized extreme value distribution and DSmT, for multi‐biometric systems

“…Likelihood ratio achieves optimal performance at all operating points on receiver operating characteristics (ROCs), provided the estimation of probability density functions for genuine and impostor distributions are accurate. Parametric density estimation methods [18, 19] assume some predefined model for genuine and impostor scores distribution which is not always accurate, whereas non‐parametric methods [20, 21] require a large number of training data for estimating the underlying distribution which is an expensive process.…”

Score-Level Fusion of Face and Voice Using Particle Swarm Optimization and Belief Functions

“…From this population, an optimal solution is found through searching and updating the past history of the particles (i.e., memories) of the population. Some examples of such approaches are: genetic algorithm [17], particle swarm optimization (PSO) [18]- [21], ant colony optimization [22], and differential evolution (DE) [23].…”