Cancelable face and iris recognition system based on deep learning

Abdellatef, Essam; Soliman, Randa F.; Omran, Eman M.; Ismail, Nabil A.; Elrahman, Salah E. S. Abd; Ismail, Khalid N.; Rihan, Mohamed; Amin, Mohamed; Eisa, Ayman A.; El‐Samie, Fathi E. Abd

doi:10.1007/s11082-022-03770-0

Cited by 19 publications

(7 citation statements)

References 33 publications

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“…The final facial descriptors are bio-convolutionally encrypted to provide user privacy and defend against spoofing attacks. Abdellatef et al [90] introduced a face and iris cancellable biometric recognition system based on a CNN model. Face and iris images are fed into the CNN model for feature extraction.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

“…Abdellatef et al. [90] introduced a face and iris cancellable biometric recognition system based on a CNN model. Face and iris images are fed into the CNN model for feature extraction.…”

Section: Feature Extraction and Learning Approachesmentioning

confidence: 99%

Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

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“…Their technique attains a fair performance besides preserving the privacy of the user. A deep learning based multi-modal cancelable biometric system is suggested by Abdellatef et al [39]. Authors considered face and iris in their approach.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter

Arepalli,

Boobalan

2024

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Biometric-based authentication systems (BAS) can provide a strong security guarantee regarding the identity of users over traditional authentication systems. The iris of the eye is widely acknowledged as one of the most robust biometrics due to its exceptional performance. Despite this, templates used in traditional iris recognition systems remain unprotected, rendering them highly susceptible to various security and privacy breaches. However, several cancelable biometric schemes being introduced but at the expense of substantially decreased accuracy performance and increased computational time. To address this, we propose a cancelable multi-instance iris authentication system utilizing a quotient filter (CMAQF). The purpose of the quotient filter in CMAQF is to distort the biometric information without compromising the accuracy. Modified local random projection is applied on the fused iris template to generate the reduced template results in less authentication time. Experiments have been conducted on publicly available iris databases to assess the efficiency of CMAQF. The experimental results conclude that CMAQF achieves reasonable performance compared to existing methods, satisfying the properties of irreversibility, diversity, and revocability.

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“…Abdellatef et al [42] worked on different areas of the face to generate cancelable biometrics. They used a feature fusion strategy with a deep CNN to create the templates.…”

Section: Related Workmentioning

confidence: 99%

Compression-Based Cancelable Multi-Biometric System

Elshazly

Hashad

Sedik

et al. 2022

Preprint

Self Cite

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The issue of cybersecurity is one of the important fields which is involved in different research trends. Biometric security is one of these trends which is involved in several applications such as access control systems and online identity verification. The protection of human biometrics can be performed using both bi-directional and unidirectional encryption. The unidirectional encryption is carried out based on cancelable biometric techniques. This paper proposes a cancelable biometric system based on image composition, deep dream, and hashing techniques. The objective of the proposed system is to generate visual and text cancelable biometrics. The visual cancelable templates are generated using image composition and deep dream, while the text templates are generated using SHA hashing techniques. The proposed system is validated by multi-biometric inputs including iris, palm, face, and fingerprint biometrics. In addition, it is evaluated in both visual and text forms. The simulation results reveal that the proposed system appears a superior performance among the works which handle this problem.

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Cancelable face and iris recognition system based on deep learning

Cited by 19 publications

References 33 publications

Feature extraction and learning approaches for cancellable biometrics: A survey

Feature extraction and learning approaches for cancellable biometrics: A survey

Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter

Compression-Based Cancelable Multi-Biometric System

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