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Dorsal hand vein recognition based on transmission-type near infrared imaging and deep residual network with attention mechanism

Abstract

Dorsal hand vein recognition, exploiting the biological information of human vein distribution structure, has the superiority of uniqueness, strong confidentiality and strong anti-forgery ability. The main challenges of the efficient dorsal hand vein recognition via usual reflection-type near infrared means are low resolution, big intra-class variation and insufficient samples. To address these issues, this paper proposed a novel dorsal hand vein recognition system based on the deep residual network with attention mechanism (DRNAM). Specifically, the improved dorsal hand vein imaging is designed by the transmission-type near infrared spectrum. Then, the system aims to extract compact and discriminative features from the dorsal hand vein image by the DRNAM model, which improves the robustness of feature extraction via cross channel and spatial information fusion. Finally, the method achieves the recognition result through the iterative training of the model. Briefly, the DRNAN model could effectively recognize the dorsal hand vein based on near infrared spectral imaging. The experimental results demonstrate that the dorsal hand vein image based on transmission-type near infrared spectrum is clearer than that based on reflection-type near infrared spectrum, and the proposed dorsal hand vein recognition method based on DRNAM outperforms the works based on the traditional convolutional neural network.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 61861020).

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Correspondence to Zhihua Xie.

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Shu, Z., Xie, Z. & Zhang, C. Dorsal hand vein recognition based on transmission-type near infrared imaging and deep residual network with attention mechanism. Opt Rev 29, 335–342 (2022). https://doi.org/10.1007/s10043-022-00750-3

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  • DOI: https://doi.org/10.1007/s10043-022-00750-3

Keywords

  • Dorsal hand vein recognition
  • Transmission-type
  • Convolution neural network
  • The deep residual network
  • The attention mechanism