Abstract
Vein recognition has been drawing more attention recently because it is highly secure and reliable for practical biometric applications. However, the underlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. To address the difficulty of direct training or fine-tuning a CNN with existing small-scale vein databases, a new knowledge transfer approach is formulated by using pre-trained CNN models together with a training dataset as a robust descriptor generation machine. A very discriminative model, sparse coding of residual descriptors (SCRD), is proposed by a hierarchical design of dictionary learning, coding, and classifier training procedures with the generated deep residual descriptors. Rigorous experiments are conducted with a high-quality hand-dorsa vein database, and superior recognition results compared with state-of-the-art models fully demonstrate the effectiveness of the proposed models. An additional experiment with the PolyU multispectral palmprint database is designed to illustrate the generalization ability.
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Acknowledgments
This work was partially supported by the Scientific Innovation 2030 Major Project for New Generation of AI under Grant 2020AAA0107300. The Fundamental Research Funds for the Central Universities under Grant 2023Q N1077.
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Shen, Z., Qin, X., Pan, Z., Wang, J. (2023). Sparse Coding of Deep Residual Descriptors for Vein Recognition. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_10
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