Contactless Hand Knuckle Modality for Identity Verification Using Siamese Network

Hammami, Siwar; Jarraya, Islem; Hamdani, Tarek M.; Alimi, Adel M.

doi:10.1109/cw58918.2023.00067

2023 International Conference on Cyberworlds (CW) 2023

DOI: 10.1109/cw58918.2023.00067

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Contactless Hand Knuckle Modality for Identity Verification Using Siamese Network

Siwar Hammami,

Islem Jarraya,

Tarek M. Hamdani

et al.

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2024

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Deep inner-knuckle-print recognition using lightweight Siamese network

Wang,

Yuan

2024

J. Electron. Imag.

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Texture features and stability have attracted much attention in the field of biometric recognition. The inner-knuckle print is unique and not easy to forge, so it is widely used in personal identity authentication, criminal detection, and other fields. In recent years, the rapid development of deep learning technology has brought new opportunities for internal-knuckle recognition. We propose a deep inner-knuckle print recognition method named LSKNet network. By establishing a lightweight Siamese network model and combining it with a robust cost function, we can realize efficient and accurate recognition of the inner-knuckle print. Compared to traditional methods and other deep learning methods, the network has lower model complexity and computational resource requirements, which enables it to run under lower hardware configurations. In addition, this paper also uses all the knuckle prints of four fingers for concatenated fusion recognition. Experimental results demonstrate that this method has achieved satisfactory results in the task of internal-knuckle print recognition.

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Deep inner-knuckle-print recognition using lightweight Siamese network

Wang,

Yuan

2024

J. Electron. Imag.

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Contactless Hand Knuckle Modality for Identity Verification Using Siamese Network

Cited by 1 publication

References 30 publications

Deep inner-knuckle-print recognition using lightweight Siamese network

Deep inner-knuckle-print recognition using lightweight Siamese network

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