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

Mezai, Lamia; Hachouf, Fella

doi:10.1109/thms.2015.2438005

Cited by 39 publications

(36 citation statements)

References 23 publications

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“…Raghavendra et al [11] proposed a PSO based fusion of near infrared and visible image for improved face verification, in the first scheme PSO is used to calculate the optimum weight of a weighted linear combination of the coefficients and in the 2 nd scheme PSO is used to select the optimal fused feature of near infrared and visible image. L. Mezai and F.Hachouf [6] proposed a fusion of face and voice using PSO and belief function at score level fusion, in which the belief assignment is generated from the score of each modality using Denoeux and Appriou models.PSO is used to estimates the confidence factor, the fusion of weighted belief assignment is carried out using Dempster-Shafer(DS) and finally make decision making whether the claim user is genuine or not. Kumar et al [12] proposed an adaptive combination of multiple biometrics classifier based on evolutionary approach in which the hybrid PSO model is used to obtain the different fusion parameter and optimal fusion strategy.…”

Section: Related Workmentioning

confidence: 99%

“…As we use score level fusion for two modalities so each ant can be represented by "N+1" dimensions where N denotes the total number of modality which is 2 in this case and the last dimension is for fusion rule chosen from Eqs (3)- (6). Each ant can be represented by , where first two parameters are the weights assigned and last parameter is the fusion rule.…”

Section: Aco For Proposed Bimodalmentioning

confidence: 99%

“…Evolutionary approaches plays an important role in multibiometric, it generate a optimum solution among a large population and finally the optimum solution is achieve through the searching and updating the past history of the particle [6].ACO (ant colony optimization), PSO (particle swarm optimization) and GA (genetic algorithm) are all comes under these category.PSO based algorithm sometime face the problem of premature convergence and its somehow effect in to get the optimum solution [7]. Score normalization play an important role in fusion process of different biometric modalities.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

Modak¹,

Jha²

2017

IJCA

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Unimodal biometric systems have several inherent problems such as intra-class variation, noisy-sensor data, spoofing attacks and non-universality. To overcome this limitation multibiometric is a good option where we can use two or more individual modalities. In this paper we propose a multibiometric system to enhance the performance and minimize the error rate using Ant colony optimization (ACO) based on score level fusion. This work extracts the feature from two different modalities namely face and iris (left/right). In this work we use ACO as an optimization technique to select the fusion parameter like weights for the different biometric matcher and fusion rule which is used further for score level fusion. The experimental results show that the multibiometric system using ACO based on sum rule is outperform than the other fusion rule like product, tanh and exponential sum.

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Section: Related Workmentioning

confidence: 99%

Section: Aco For Proposed Bimodalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

Modak¹,

Jha²

2017

IJCA

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“…Though the methods proposed in [6,36] shows a significant performance improvement, they are too complex for real-world implications. Besides feature level, there have been various methods [22,15,18,9,25] incorporating score fusion in recent years. Mezai et al [22] applied score fusion over face and voice biometric using DS theory.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification

Dwivedi

Dey

2018

Appl Intell

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In spite of the benefits of biometric-based authentication systems, there are few concerns raised because of the sensitivity of biometric data to outliers, low performance caused due to intra-class variations and privacy invasion caused by information leakage. To address these issues, we propose a hybrid fusion framework where only the protected modalities are combined to fulfill the requirement of secrecy and performance improvement. This paper presents a method to integrate cancelable modalities utilizing mean-closure weighting (MCW) score level and Dempster-Shafer (DS) theory based decision level fusion for iris and fingerprint to mitigate the limitations in the individual score or decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the similarity scores from different matchers corresponding to each protected modality. The individual scores obtained from different matchers for each modality are combined using MCW score fusion method. The MCW technique achieves the optimal weight for each matcher involved in the score computation. Further, DS theory is applied to the induced scores to output the final decision. The rigorous experimental evaluations on three virtual databases indicate that the proposed hybrid fusion framework outperforms over the component level or individual fusion methods (score level and decision level fusion). As a result, we achieve (48%,66%), (72%,86%) and (49%,38%) of performance improvement over unimodal cancelable iris and unimodal cancelable fingerprint verification systems for Virtual A, Virtual B and Virtual C databases, respectively. Also, the proposed method is robust enough to the variability of scores and outliers satisfying the requirement of secure authentication.

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“…According to the signatures' acquisition method, signature verification systems can be categorized into biometric applications [24], [36][37][38][39], where conflicting results are the common scenario. Although combining multiple classifiers is a widely used technique in the field of signature verification, to the best of the authors' knowledge, the degree of uncertainty that this technique may introduce to the classification problem has not been significantly explored.…”

Section: Introductionmentioning

confidence: 99%

Online Signature Verification: To What Extent Should a Classifier be Trusted in?

Parodi¹,

Gómez²

2017

CLEIej

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To select the best features to model the signatures is one of the major challenges in the field of online signature verification. To combine different feature sets, selected by different criteria, is a useful technique to address this problem. In this line, the analysis of different features and their discriminative power has been researchers' main concern, paying less attention to the way in which the different kind of features are combined. Moreover, the fact that conflicting results may appear when several classifiers are being used, has rarely been taken into account. In this paper, a score level fusion scheme is proposed to combine three different and meaningful feature sets, viz., an automatically selected feature set, a feature set relevant to Forensic Handwriting Experts (FHEs), and a global feature set. The score level fusion is performed within the framework of the Belief Function Theory (BFT), in order to address the problem of the conflicting results appearing when multiple classifiers are being used. Two different models, namely, the Denoeux and the Appriou models, are used to embed the problem within this framework, where the fusion is performed resorting to two well-known combination rules, namely, the Dempster-Shafer (DS) and the Proportional Conflict Redistribution (PCR5) one. In order to analyze the robustness of the proposed score level fusion approach, the combination is performed for the same verification system using two different classification techniques, namely, Ramdon Forests (RF) and Support Vector Machines (SVM). Experimental results, on a publicly available database, show that the proposed score level fusion approach allows the system to have a very good trade-off between verification results and reliability.

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

Cited by 39 publications

References 23 publications

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification

Online Signature Verification: To What Extent Should a Classifier be Trusted in?

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