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Palm vein recognition through fusion of texture-based and CNN-based methods

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Abstract

In this paper, we propose a palm vein recognition system that combines two approaches using a decision-level fusion strategy. The first approach employs Binarized Statistical Image Features (BSIF) descriptor method on five overlapping sub-regions of palm vein images and the second approach uses a convolutional neural networks (CNN) model on each palm vein image. In the first approach, texture-based features of five overlapping sub-regions on the palm vein image are extracted using the powerful BSIF method and the scores obtained by the matching step of the system are fused with score-level fusion strategy. In the second approach, a CNN model is used to train the system using the whole image. Afterwards, the decisions of two approaches are gathered separately and a final decision is obtained by fusing the two decisions. Experimental results on CASIA, FYO, PUT, VERA and Tongji Contactless Palm Vein databases showed that the proposed method compared favorably against other similar systems.

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Acknowledgements

Portions of the research in this paper used the CASIA-MS-PalmprintV1 collected by the Chinese Academy of Sciences’ Institute of Automation (CASIA). We also used PUT Vein Pattern Database provided by CIE Biometrics. VERA palm vein database was provided by The Idiap Research Institute, Martigny and Haute Ecole Spécialisée de Suisse Occidentale in Sion, in Switzerland. A Large-scale contactless palm vein image dataset was provided by Lin Zhang, Zaixi Cheng, Ying Shen, and Dongqing Wang of School of Software Engineering, Tongji University.

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  1. Computer Engineering Department, Faculty of Engineering, Eastern Mediterranean University, Famagusta, 99628, North Cyprus, via Mersin 10, Turkey

    Felix Olanrewaju Babalola, Yıltan Bitirim & Önsen Toygar

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  1. Felix Olanrewaju Babalola
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  2. Yıltan Bitirim
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  3. Önsen Toygar
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Correspondence to Önsen Toygar.

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Babalola, F.O., Bitirim, Y. & Toygar, Ö. Palm vein recognition through fusion of texture-based and CNN-based methods. SIViP 15, 459–466 (2021). https://doi.org/10.1007/s11760-020-01765-6

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