Abstract
Finger is a very competent biometric carrier, which contains many valuable biometric features (fingerprint, finger-vein and finger-knuckle-print). Exploring multi-modal biometric fusion recognition of fingers is very meaningful for improving the security and stability of finger feature expression. Due to the differences in the expression of different modal features, the interaction mode and fusion strategy between different modalities are the key to finger multi-modal biometrics. Recently, we note that Transformer exhibits strong performance in natural language processing and computer vision. In this paper, a Transformer based finger tri-modal fusion and recognition framework, termed TransFinger, is proposed, where Transformer attention is employed to calculate the interaction response among the three modalities of the finger, and from this, the channel and spatial attentions of different modalities are generated. To the best of our knowledge, it is the first to apply Transformer attention to implement the fusion and recognition of finger biometrics. At the same time, with only minor changes, the TransFinger framework has good generalization for the number of fused modalities. Simulation results have demonstrated the effectiveness of the proposed finger tri-modal fusion and recognition framework.
This work was supported by the Shenzhen Science and Technology Program (No. RCBS20200714114940262), National Natural Science Foundation of China (No. 61806208 and 62076166).
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Zhao, Z., Zhang, H., Chen, Z., Yang, J. (2022). TransFinger: Transformer Based Finger Tri-modal Biometrics. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_12
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