An Improved Method for Generating Biometric-Cryptographic System from Face Feature

Aanjanadevi, S.; Palanisamy, V.; Aanjankumar, S.

doi:10.1109/icoei.2019.8862741

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Recent studies have proposed various techniques to generate cryptographic keys from biometric modalities such as face, fingerprint, and EEG signals [67][68][69][70]. These techniques typically involve extracting features from biometric data using methods such as PCA, Gabor filters, ICA, or deep CNNs.…”

Section: Cryptographic Key Generation Using Biometricsmentioning

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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Section: Cryptographic Key Generation Using Biometricsmentioning

confidence: 99%

The Emerging Challenges of Wearable Biometric Cryptosystems

Ajlan,

Alsboui,

Alshaikh

et al. 2024

Cryptography

View full text Add to dashboard Cite

show abstract

“…The authors of [11] proposed employing CBRS for biometric credentials in cloud computing facilities, which is based on deep learning. A CBRS for multi-biometric authentication and validation was proposed by the authors of [12]. In the proposed CBRS, a surreptitious produced key from another biometric picture is used to convert a biometric doppelgänger to a safe and cancellable biometric pattern using resourceful bit-wise encryption methodologies.…”

Section: Related Workmentioning

confidence: 99%

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Aanjanadevi¹,

Aanjankumar²,

Ramela³

et al. 2023

Computer Systems Science and Engineering

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Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.

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A Biometric Key-Enhanced Multimedia Encryption Algorithm for Social Media Blockchain

Liu,

2024

J CIRCUIT SYST COMP

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Social media blockchain is emerging as a promising solution to deal with privacy issues, by putting user privacy in edge nodes rather than centralized nodes. Under the protection of information encryption, only those who have cryptographic keys can get access to key information. This work aims at multimedia information in social media blockchain and utilizes the RSA encryption mechanism to construct the information encryption system. Due to the resilience of biometric features, the biometric cryptographic keys are not easy to be fabricated. Thus, this paper proposes a biometric keys-enhanced multimedia encryption algorithm for social media blockchain. First of all, the wiener filter is adopted to make some preprocessing operations to images, such as noise reduction. On this basis, the discrete wavelet transform is adopted to extract feature representation from images, and nonlinear approximation of contourlet transform is adopted to make feature fusion. Next, cryptographic keys can be generated from the fused biometric feature vectors to encrypt biometric data. Finally, some simulation experiments are conducted to evaluate performance of the proposal from three aspects: key generation time, security level and encryption-decryption time complexity. For key generation time, processing speed of the proposal is approximately 1–2[Formula: see text]ms per sample. For security level, the proposal can reach an index value beyond 95% which is higher than comparison methods. For encryption-decryption time complexity, the proposal is about 30% lower than comparison methods.

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An Improved Method for Generating Biometric-Cryptographic System from Face Feature

Cited by 4 publications

References 7 publications

The Emerging Challenges of Wearable Biometric Cryptosystems

The Emerging Challenges of Wearable Biometric Cryptosystems

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

A Biometric Key-Enhanced Multimedia Encryption Algorithm for Social Media Blockchain

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