Abstract
In finger-vein recognition tasks, obtaining large labeled datasets for supervised deep learning is often difficult. To address this challenge, self-supervised learning (SSL) provides a solution by first pre-training a neural network using unlabeled data and subsequently fine-tuning it for downstream tasks. Contrastive learning, a variant of SSL, enables effective learning of image-level representations. To address the issue of insufficient labeled data for vein feature extraction and classification, we propose CL3A-FV, a Contrastive Learning-based Finger-Vein image recognition approach with Automatic Adversarial Augmentation in this paper. Specifically, CL3A-FV consists of the dual-branch augmentation network, Siamese encoder, discriminator, and distributor. The training process involves two steps: 1) training the Siamese encoder by updating its parameters while keeping other components fixed; and 2) training the dual-branch augmentation network with a fixed Siamese encoder, integrating a discriminator to distinguish views generated by the two branches, and a distributor to constrain the distribution of the augmented data. Both networks are updated adversarially using the stochastic gradient descent. We conduct extensive experiments to evaluate CL3A-FV on three finger-vein datasets, and the experimental results show that the proposed CL3A-FV achieves significant improvements compared to traditional self-supervised learning techniques and supervised methods.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grants 62072061 and 61976030, and in part by the Funds for Creative Research Groups of Chongqing Municipal Education Commission under Grant CXQT21034.
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Deng, S., Luo, H., Qin, H., Li, Y. (2024). Contrastive Learning-Based Finger-Vein Recognition with Automatic Adversarial Augmentation. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_27
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