A fingerprint and finger-vein based cancelable multi-biometric system

Yang, Wencheng; Wang, Song; Hu, Jiankun; Zheng, Guanglou; Valli, Craig

doi:10.1016/j.patcog.2018.01.026

Cited by 179 publications

(88 citation statements)

References 20 publications

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“…To further enhance security and recognition performance, some researchers proposed use of multimodal cancelable biometrics. For example, Yang et al [50] proposed a multimodal cancelable biometric system that fuses fingerprint features and finger-vein features to achieve better recognition accuracy and higher security. In the proposed system, an enhanced partial discrete Fourier transform is utilized to provide non-invertibility and revocability.…”

Section: Cancelable Biometricsmentioning

confidence: 99%

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Security and Accuracy of Fingerprint-Based Biometrics: A Review

et al. 2019

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Biometric systems are increasingly replacing traditional password-and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper. recognition as an example. In the stage of enrollment, a user presents their finger to the fingerprint sensor and a fingerprint image is acquired by the sensor module. Certain features of the acquired fingerprint image are extracted, and further adapted or transformed to generate template data for the purpose of comparison in the verification stage. In the verification stage, the fingerprint image of a query is collected by the sensor module. The feature representations of the query fingerprint image go through the same process as in the enrollment stage, so as to obtain query data. The query data are then compared with the template data so that a matching outcome is attained.

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Section: Cancelable Biometricsmentioning

confidence: 99%

“…In this section, the evolution of cancelable biometrics, from the introduction of the idea of cancelable biometrics and some early transformation function designs [33], to the recent multiple cancelable biometrics [50], is presented. There are two categories in the design of cancelable biometrics.…”

Section: Cancelable Biometricsmentioning

confidence: 99%

Security and Accuracy of Fingerprint-Based Biometrics: A Review

et al. 2019

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“…The performance of proposed approach goes lower to higher w.r.t high dimension bitmaps as the number of the available bits increased. Experiment-2 : Inspired from the earlier works [17], [21], we propose to concatenate the deep feature vectors of major knuckle, minor knuckle and nail to quantitatively evaluate the performance of cancelable finger dorsal biometrics. This test has been performed on PolyU FKI database with the same testing strategy.…”

Section: Algorithm 1: Verification Algorithmmentioning

confidence: 99%

“…With no pre-alignment [20], authors proposed to exploit zoned minutia feature into non-invertible fingerprint representation by using partial DFT(Discrete Fourier transform). In [21], authors presented a cancelable multi-biometric systems in which fused features of fingerprint and finger vein are transformed into common template by using enhanced partial discrete Fourier transform. To the best of our knowledge [14] is the only paper in which CNN is used to learn robust binary representation of deep face features which are further hashed using cryptographic method known as SHA-3 512.…”

Section: Introductionmentioning

confidence: 99%

FDFNet: A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing

Singh

Arora

Patel

et al. 2019

2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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Present world has already been consistently exploring the fine edges of online and digital world by imposing multiple challenging problems/scenarios. Similar to physical world, personal identity management is very crucial inorder to provide any secure online system. Last decade has seen a lot of work in this area using biometrics such as face, fingerprint, iris etc. Still there exist several vulnerabilities and one should have to address the problem of compromised biometrics much more seriously, since they cannot be modified easily once compromised. In this work, we have proposed a secure cancelable finger dorsal template generation network (learning domain specific features) secured via. Bio-Hashing. Proposed system effectively protects the original finger dorsal images by withdrawing compromised template and reassigning the new one. A novel Finger-Dorsal Feature Extraction Net (FDFNet) has been proposed for extracting the discriminative features. This network is exclusively trained on trait specific features without using any kind of pre-trained architecture. Later Bio-Hashing, a technique based on assigning a tokenized random number to each user, has been used to hash the features extracted from FDFNet. To test the performance of the proposed architecture, we have tested it over two benchmark public finger knuckle datasets: PolyU FKP and PolyU Contactless FKI. The experimental results shows the effectiveness of the proposed system in terms of security and accuracy.

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“…In recent years, Rathgeb et al [29] proposed Bloom filter based integration scheme to combine face and iris features. Yang et al [39] proposed a method to integrate fingerprint and finger-vein features thereby improving recognition accuracy and security. They evaluated invariant features and concatenated the features after dividing the features into blocks.…”

Section: Related Workmentioning

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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A fingerprint and finger-vein based cancelable multi-biometric system

Cited by 179 publications

References 20 publications

Security and Accuracy of Fingerprint-Based Biometrics: A Review

Security and Accuracy of Fingerprint-Based Biometrics: A Review

FDFNet: A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing

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

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