Score-level fusion for cancelable multi-biometric verification

Dwivedi, Rudresh; Dey, Somnath

doi:10.1016/j.patrec.2018.04.022

Cited by 28 publications

(20 citation statements)

References 23 publications

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“…To validate the performance of our method, we compare our proposed hybrid fusion scheme with other recent methodologies in literature. Besides hybrid fusion methods [12,37], we include few other recent state-of-the-art fusion approaches based on score level [25,18,15,22,9] and decision level fusion [31,17]. As described in performance evaluation, it can be observed that the proposed method performs optimally than the other approaches with respect to EER (see Figure 4 -6).…”

Section: Statistical Evaluation Of Proposed Hybrid Fusion Methodsmentioning

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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Section: Statistical Evaluation Of Proposed Hybrid Fusion Methodsmentioning

confidence: 99%

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

Dwivedi

Dey

2018

Appl Intell

Self Cite

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“…A statistical analysis of unprotected templates is carried out to estimate the main parameters of such schemes. Dwivedi & Dey (2019) proposed a method to obtain cancelable templates by using log-Gabor filters with phase quantization, followed by the generation of biometric codes. Score level fusion from multiple biometric templates are used for authentication yielding better results in comparison to unibiometric systems with better accuracy.…”

Section: Social Acceptancementioning

confidence: 99%

Hand-based multibiometric systems: state-of-the-art and future challenges

Aftab

Khan

et al. 2021

PeerJ Computer Science

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The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

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“…ROC of the dual iris system Table 3. Accuracy and error for vaious approaches Iris recognition systems Accuracy (%) FAR (%) FRR (%) EER (%) Iftakhar and Ashraful Approach [11] 99.92 0.00 9.96 -DST approach [12] ---1 R. Dwivedi and S. Dey [13] 98.89 --0.69 Proposal dual iris authentication system 99.96 0.00 3.89 2…”

Section: S Rmentioning

confidence: 99%

“…R. Dwivedi and S. Dey [13] proposed a cancelable multibiometric system using score level fusion. The fusion of scores was applied by MC weighting at first level and RA weighting at the second level.…”

mentioning

confidence: 99%

Dual iris authentication system using dezert smarandache theory

Ghalem¹,

Hendel²

2019

IJECE

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In this paper, a dual iris authentication using Dezert Smarandache theory is presented. The proposed method consists of three main steps: In the first one, the iris images are segmented in order to extract only half iris disc that contains relevant information and is less affected by noise. For that, a Hough transform is used. The segmented images are normalized by Daugman rubber sheet model. In the second step, the normalized images are analyzed by a bench of two 1D Log-Gabor filters to extract the texture characteristics. The encoding is realized with a phase of quantization developed by J. Daugman to generate the binary iris template. For the authentication and the similarity measurement between both binary irises templates, the hamming distances are used with a previously calculated threshold. The score fusion is applied using DSmC combination rule. The proposed method has been tested on a subset of iris database CASIA-IrisV3-Interval. The obtained results give a satisfactory performance with accuracy of 99.96%, FAR of 0%, FRR of 3.89%, EER of 2% and processing time for one iris image of 12.36 s.<em> </em>

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Score-level fusion for cancelable multi-biometric verification

Cited by 28 publications

References 23 publications

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

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

Hand-based multibiometric systems: state-of-the-art and future challenges

Dual iris authentication system using dezert smarandache theory

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