Abstract
Bimodal palmprint recognition use palm vein and palmprint images at the same time, which can achieve high accuracy and has intrinsic anti-falsification property. For bimodal palmprint recognition and verification, the ROI detection and ROI alignment of palmprint region-of-interest (ROI) are two crucial points for bimodal palmprint matching. Most existing plamprint ROI detection methods are based on keypoint detection algorithms, however the intrinsic difficulties lying in keypoint detection tasks make the results not accurate. Besides, in these methods the ROI alignment and feature fusion algorithms at image-level are not fully investigated. To improve the performance and bridge the gap, we propose our Bimodal Palmprint Fusion Network (BPFNet) which focuses on ROI localization, alignment and bimodal image fusion. BPFNet is an end-to-end deep learning framework which contains two parts: The detection network directly regresses the palmprint ROIs and conducts alignment by estimating translation. In the downstream, the fusion network conducts bimodal ROI image fusion leveraging a novel cross-modal selection scheme. To demonstrate the effectiveness of BPFNet, we implement experiments on two touchless palmprint datasets and the proposed framework achieves state-of-the-art performances.
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Acknowledgement
This work is supported by Shenzhen Institute of Artificial Intelligence and Robotics for Society. The work is also supported in part by the NSFC under Grants 6217070450 and 62076086, Shenzhen Science and Technology Program (RCBS20200714114910193), Open Project Fund from Shenzhen Institute of Artificial Intelligence and Robotics for Society (AC01202005017).
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Li, Z., Liang, X., Fan, D., Li, J., Zhang, D. (2021). BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusion. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_4
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