Fingerprint bio‐key generation based on a deep neural network

Wu, Zihao; Lv, Zhengyin; Kang, Jie; Ding, Wenqian; Zhang, Jianwu

doi:10.1002/int.22782

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…Practical instances of fuzzy extractors need to be carefully assessed in terms of their entropy output, both in terms of intra-user and inter-user aspects. An interesting approach worthy of further investigation is to explore the efficacy of machine learning when performing biological readings for cryptographic key generation [11].…”

Section: Discussionmentioning

confidence: 99%

Threshold Key Storage via Fuzzy Extractors With Applications

Mullan

2023

Cryptography and Blockchain

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We propose a novel threshold key storage scheme that relies on biometric fuzzy extractors for the derivation of user keys. This approach builds upon the existing framework of password-protected secret sharing constructions, offering a potential improvement in security and user convenience. Usually in such schemes, users are required to generate and remember passwords, which in practice can introduce vulnerabilities and usability issues. By removing this reliance on traditional passwords, our scheme may enhance the overall security of threshold key storage solutions. Moreover, in situations where password recovery is not a viable option, our mechanism provides a dependable solution for online private key storage, ensuring users can access their keys securely and reliably.

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Section: Discussionmentioning

confidence: 99%

Threshold Key Storage via Fuzzy Extractors With Applications

Mullan

2023

Cryptography and Blockchain

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“…[ 29 , 30 ] decreased the accuracy during face recognition due to the suppression of soft biometric information in the face image. Wu et al [ 31 , 32 ] proposed the technical idea of using biometric key technology to directly generate a strong biometric key from the biometric characteristics of the client, which means the server does not need to save the biometric template, so as to protect privacy. At present, the stability of the key generated by this technical route needs to be improved.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning

Huang,

Wu,

Chen

et al. 2024

J. Imaging

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Personal privacy protection has been extensively investigated. The privacy protection of face recognition applications combines face privacy protection with face recognition. Traditional face privacy-protection methods encrypt or perturb facial images for protection. However, the original facial images or parameters need to be restored during recognition. In this paper, it is found that faces can still be recognized correctly when only some of the high-order and local feature information from faces is retained, while the rest of the information is fuzzed. Based on this, a privacy-preserving face recognition method combining random convolution and self-learning batch normalization is proposed. This method generates a privacy-preserved scrambled facial image and an image fuzzy degree that is close to an encryption of the image. The server directly recognizes the scrambled facial image, and the recognition accuracy is equivalent to that of the normal facial image. The method ensures the revocability and irreversibility of the privacy preserving of faces at the same time. In this experiment, the proposed method is tested on the LFW, Celeba, and self-collected face datasets. On the three datasets, the proposed method outperforms the existing face privacy-preserving recognition methods in terms of face visual information elimination and recognition accuracy. The recognition accuracy is >99%, and the visual information elimination is close to an encryption effect.

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“…The experimental results show that the key generation performance has a 98.47% GAR and 1% FAR. In 2022, Wu et al [13] designed a suitable multilayer convolutional projection fingerprint biokey generation model for generating the fingerprint biokey. The system can effectively eliminate the instability between fingerprint samples using feature selection and layer-by-layer convolution projection characteristics from deep neural networks.…”

Section: Related Workmentioning

confidence: 99%

A Secure Biometric Key Generation Based on Coordinate Attention Mechanism And Reliable Feature Selection

Zhou

Wang

2023

Preprint

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With the development of the information era, the study of biometric keys has attracted increasing attention. However, the performance and safety of the proposed methods seem challenging to meet the needs of real applications. To solve this problem, this paper presents a finger vein biometric key system. The proposed system proposes an improved network in the feature extraction stage, which combines the coordinate attention mechanism and SkipNet to obtain richer feature information. An improved, reliable feature selection method is introduced to acquire more discriminative finger vein features. A Lagrange interpolation method then binds the user's finger vein features and keys. Finally, experiments are carried out on the public USM, Poly, and Net64 databases. The results indicate that we acquire a 1024-bit key with 0.049% FAR and 0% FRR in Net64 and 0% FAR and FRR in USM and Poly. Additionally, the system's security is analyzed from key randomness, system information leak attack, brute force attack, cross-matching attack, and spoofing attack. Experimental and theoretical analyses show that the proposed system has good accuracy and security.

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Fingerprint bio‐key generation based on a deep neural network

Cited by 9 publications

References 36 publications

Threshold Key Storage via Fuzzy Extractors With Applications

Threshold Key Storage via Fuzzy Extractors With Applications

Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning

A Secure Biometric Key Generation Based on Coordinate Attention Mechanism And Reliable Feature Selection

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