Abstract
Deep learning-based Biometric authentication has become one of the most popular research subjects in the field of Computer Vision. In this paper, we propose a novel model architecture for finger vein recognition based on an improved residual attention network. First, we squeeze the size of the original network to adapt to the training data scale. Then, to prevent excessively repeated operations of linear extraction, we introduce the Inception unit to replace some residual units in the original model. The multi-branch structure can learn vein features from different aspects. Besides that, with the attention block, primary vein patterns can be extracted and the bottom-up, top-down structure activates feature maps with learned attention weights. The experimental results show that our model acquires 98.58% and 97.54% accuracy on two public datasets, respectively. Compared with state-of-the-art models, the proposed model has fewer parameters and better performance.
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Acknowledgments
This research is sponsored by the Key R&D Program of Science and Technology Development Plan of Jilin Province of China (No. 20200401103GX); the Key Program of Science and Technology Research during the 13th Five-Year Plan Period, the Educational Department of Jilin Province of China (No. JJKH20200680KJ, and No. JJKH20200677KJ); and the National Natural Science Foundation of China (No. 61806024).
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Liu, W., Lu, H., Li, Y., Wang, Y., Dang, Y. (2021). An Improved Finger Vein Recognition Model with a Residual Attention Mechanism. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_26
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