A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

Sardar, Alamgir; Umer, Saiyed; Rout, Ranjeet Kumar; Khan, Muhammad Khurram

doi:10.1109/tai.2022.3188596

Cited by 9 publications

(3 citation statements)

References 62 publications

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“…The data points are then assigned to clusters whose centroids are closest. Sardar et al [106] proposed a cancellable palmprint recognition system with good performance and enhanced template protection. In this system, a 200 � 200 palm region is first extracted from the input image in a pre-processing process.…”

Section: Principal Component Analysismentioning

confidence: 99%

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: Principal Component Analysismentioning

confidence: 99%

Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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“…Imran et al [19] proposed the ElGamal algorithm for the encryption-decryption of speech signals. Sardar et al [20] had proposed a palmprint recognition using statistical patch based feature representation technique and also introduced a non-invertible BioCryptosystems to preserve biometric templates. Dissanayake et al [21] improve the ElGamal to achieve better security which avoids plain text attacks.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment

Sardar,

Umer,

Rout

et al. 2024

IEEE Internet Things J.

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With the increasing use of biometrics in Internet of Things (IoT) based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: face recognition and face biometric template protection. The face recognition includes face image preprocessing by the Tree Structure Part Model (TSPM), feature extraction by Ensemble Patch Statistics (EPS) technique, and user classification by multi-class linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR) based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-dimensional cancelable feature vectors computed from the 500-dimensional original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.

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“…In general, biometrics can be integrated into cryptography in one of three ways: key release, key binding, or key generation. Using the key release mode completely separates the key release mechanism from the biometric template matching process [22]. In the smart card, token, or computer where they are both stored, the biometric template and the cryptographic key are kept as separate entities.…”

Section: Introductionmentioning

confidence: 99%

The Emerging Challenges of Wearable Biometric Cryptosystems

Ajlan,

Alsboui,

Alshaikh

et al. 2024

Cryptography

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Cryptographic key generation and data encryption and decryption using wearable biometric technologies is an emerging research area with significant potential for authentication and communication security. The research area is rapidly developing, and a comprehensive review of recently published literature is necessary to establish emerging challenges. This research article aims to critically investigate and synthesize current research using biometric cryptosystems that use behavior or medico-chemical characteristics, ranging from gate analysis to gaze tracking. The study will summarize the state of knowledge, identify critical research gaps, and provide insight into promising future implications and applications that can enable the realization of user-specific and resilient solutions for authentication and secure communication demands.

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A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

Cited by 9 publications

References 62 publications

Feature extraction and learning approaches for cancellable biometrics: A survey

Feature extraction and learning approaches for cancellable biometrics: A survey

Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment

The Emerging Challenges of Wearable Biometric Cryptosystems

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