Score level fusion algorithm using Differential Evolution and Proportional Conflict Redistribution rule

Mezai, Lamia; Hachouf, Fella

doi:10.1109/smc.2016.7844381

Cited by 2 publications

(5 citation statements)

References 6 publications

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“…Multi-spectral palmprint Different t-conorm compared to sum, SVM [8] Iris, face GA features selectionSVM score fusion [9] Speech, lipreading Average [30] NIST BSSR1 multi-biometric score database (fingerprint and face), face, iris dataset Dezert-Smarandache theory DSmT [15] Palm/phalanges print Sum, product, min, max, hamacher t-norm, frank t-norm, yager's t-norm [5] Face, palmprint, signature, speech Sum rule compared to the unimodal system [31] Fingerprint, iris, left ear and right ear DE, exponent control, and Kernel mapping [19] Fingerprint and voice DE and proportional conflict redistribution [20] Face and voice PSO, Dempster Shafer and Belief functions [21] Two face databases Quasi-convex optimization [22] Iris, finger vein and fingerprint Evolutionary backtracking search optimization algorithm [25] Abbreviation: GA, Genetic Algorithm. only one contains two traits (NIST BSSR1), and the two other databases (FRGC, IRIS dataset) are used for multi-algorithm and multi-sample fusion (Table 2).…”

Section: Modalities Methods Referencesmentioning

confidence: 99%

“…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. The belief assignments representing the transformed scores are then fused using proportional conflict redistribution (PCR).…”

Section: Score Level Fusionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Optimized multi‐biometric enhancement analysis

et al. 2021

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A multi‐biometric system uses different modalities to identify individuals more accurately. The authors analyse fusion efficiency of a significant number of multi‐biometric fusion schemes. To do so, the study applies different functions that are generated using genetic programming (GP) on the 2000 multi‐biometric instances produced by the fusion of different biometric matching scores. The functions are represented using a tree of arithmetic operations and are used for fusion at score level. First, genetic programming is implemented on the XM2VTS score database. The GP optimizes the half total error rate of fused matching scores. Then, a comparative study is performed based on our experiments on matching scores of different biometric baseline systems provided by the bio‐secure database. This database provides 24 streams that we use to generate 2000 multi‐biometric combinations. These multi‐biometric instances combine matching scores of different instances, sensors and traits. To assess the quality of the fused scores and the quality of performing biometric baseline systems, we use weighted functions based on user‐specific and group‐specific normalization. Then, we propose a hybrid cat swarm optimization (CSO) based on the average‐velocity inertia‐weighted CSO and the normal mutation strategy‐based CSO to compute the weights of the selected functions for the fused biometric systems. Finally, we present the statistical significance tests to confirm that the proposed functions outperform the existing functions based on arithmetic rules, normalization fusion and evolutionary algorithms.

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Section: Modalities Methods Referencesmentioning

confidence: 99%

Section: Score Level Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimized multi‐biometric enhancement analysis

et al. 2021

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“…In [16], Kien et al proposed a score level fusion technique based on D-S theory and uncertain factors. The research seeks to develop a unified framework for multimodal biometric fusion to take advantages of the uncertainty concept of the D-S theory.…”

Section: Related Workmentioning

confidence: 99%

Multimodal biometrics Fusion based on TER and Hybrid Intelligent Multiple Hidden Layer Probabilistic Extreme Learning Machine

Wu¹,

Wan²

2018

IJCIS

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In this paper, a novel fusion method based on Total Error Rate (TER) and multiple hidden layer probabilistic extreme learning machine is proposed. At first, the study transfers the matching scores into TER based on corresponding False Reject Rates (FRR) and False Accept Rates (FAR) aims at avoiding to calculating the posterior probability. At the second, a new fusion strategy based on multiple hidden layer probabilistic extreme learning machine is introduced, which optimizes the architecture of hidden nodes by weighted calculation of different output matrices and then transforms the numeric output of ELM to the probabilistic outputs and unifies the outputs in a fixed range, the matrices weights and the output weights are optimized using a hybrid intelligent algorithm based on differential evolution and particle swarm optimization. Experiment result shown that the proposed method renders very good performance as it is quite computationally and outperforms the traditional score level fusion schemes, the experimental result also confirms the effectiveness of the proposed method to improve the performance of multibiometric system.

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Score level fusion algorithm using Differential Evolution and Proportional Conflict Redistribution rule

Cited by 2 publications

References 6 publications

Optimized multi‐biometric enhancement analysis

Optimized multi‐biometric enhancement analysis

Multimodal biometrics Fusion based on TER and Hybrid Intelligent Multiple Hidden Layer Probabilistic Extreme Learning Machine

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