Competing texture directions and continuous surface types for 3D palmprint recognition

Sen Lin; Peng Shang

doi:10.1117/12.2675110

25 May 2023 Competing texture directions and continuous surface types for 3D palmprint recognition

Sen Lin, Peng Shang

Author Affiliations +

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126361B (2023) https://doi.org/10.1117/12.2675110
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Three dimensional(3D) palmprint recognition is a prospective biometric recognition technology, and texture direction is one of its main features. Firstly, the texture direction extracted by the Shape Index(SI) is susceptible to noise interference, and to solve this problem, the noise is eliminated by convolution operation, meanwhile the texture direction is extracted using competitive coding. Secondly, the traditional Surface Type(ST) tends to ignore the small curvature feature information, while the palmprint adjacent points have the same curvature, so a Continuous Surface Type(CST) is proposed to obtain surface consistency. Finally, the method of Collaborative Representation(CR) is used to combine features to complete palmprint recognition in classification recognition. The results of the experiment on the 3D palmprint library of the Hong Kong Polytechnic University show that the average recognition rate obtained can reach 99.92%, and the average recognition time is 0.032s compared with other classification methods. The proposed method can improve the recognition accuracy of 3D palmprint while maintaining low recognition time, and the feature extraction ability and classification recognition effect are of practical value.

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Sen Lin and Peng Shang "Competing texture directions and continuous surface types for 3D palmprint recognition", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126361B (25 May 2023); https://doi.org/10.1117/12.2675110

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KEYWORDS

Feature extraction

Education and training

Databases

Detection and tracking algorithms

Tunable filters

Convolution

Deep learning

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