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Multi-Scale Arc-Fusion Based Feature Embedding for Small-Scale Biometrics

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Abstract

Automatic person recognition using finger-vein has been extensively investigated in recent years. Mostly the published research on biometrics are based on the hand-crafted feature descriptors to describe the appearance of a finger-vein image, which is prone to be affected by irregular shading, irrelevant background, varying illumination and non-rigid deformation. To improve the recognition performance of hand-crafted feature-based methods, many researchers have contributed substantially in designing enhancement methods for texture descriptors. However, hand-crafted algorithms are highly targeted with weak generalization ability with respect to the emerging data samples. Nowadays, deep convolutional neural networks (CNN) is emerging as a powerful technology to extract multi-level feature representation instead of traditionally designed features from raw data. These methods are achieving unprecedented performance in the field of computer vision. In context to biometrics modalities, finger-vein recognition using CNN is still in its primary stage. In this paper, we proposed a simple yet effective multi-scale arc-fusion approach to optimize the feature embedding discrimination power. An exhaustive comparative experiment is conducted on four publicly available databases: HKPU, FV-USM, SDUMLA and UTFVP, to demonstrate that the proposed approach is superior to the existing state-of-the-art (SOTA) methods. Also, the experimental result shows that the concept is actually scalable and can be migrated to other deep neural network architectures.

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Notes

  1. [online] HKPU https://www4.comp.polyu.edu.hk/~csajaykr/fvdatabase.htm.

  2. [online] FV-USM http://drfendi.com/fv_usm_database/.

  3. [online] SDUMLA http://mla.sdu.edu.cn/info/1006/1195.htm.

  4. [online] UTFVP https://www.utwente.nl/en/eemcs/dmb/downloads/utfvp/.

  5. [online] PyTorch https://github.com/pytorch/pytorch.

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Acknowledgements

The authors would like to thank The Hong Kong Polytechnic University, Universiti Sains Malaysia, Shandong University and University of Twente for sharing the finger-vein databases.

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  1. Shitala Prasad and Tingting Chai have contributed equally to this work.

Authors and Affiliations

  1. Visual Intelligence, I2R, A*Star, Singapore, 138632, Singapore

    Shitala Prasad

  2. Faculty of Computing, HIT, Harbin, 150001, China

    Tingting Chai

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  1. Shitala Prasad
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  2. Tingting Chai
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Correspondence to Shitala Prasad.

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Prasad, S., Chai, T. Multi-Scale Arc-Fusion Based Feature Embedding for Small-Scale Biometrics. Neural Process Lett 55, 8829–8846 (2023). https://doi.org/10.1007/s11063-023-11179-3

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