Abstract：Deep learning-based methods show excellent recognition performance and potential in finger vein recognition. However, due to the expensive training costs and differences in categories and distributions across different datasets, a model that performs well on one dataset may struggle to efficiently adapt to new data or perform poorly on new data. A fast transferable model for cross-dataset finger vein recognition, including a two-stage solution, is proposed to realize high efficient application of model on different datasets with good performance in practical scenarios where recognition systems are applied to various user groups and devices. Firstly, in the first stage, a domain adaptation algorithm based on feature alignment and clustering is introduced to guide the network in extracting discriminative and robust features, aiming to obtain a deep model that can generalize well on unseen target data. Secondly, a bias field correction network is developed to reduce dataset gaps caused by bias fields in images and further adjust the latent distribution to make the datasets more similar to each other. Then, in the second stage of fast transfer, a modified classifier based on extreme learning machine with a faster learning speed is designed to accelerate model transfer training and make full use of the template information of new data.Experimental results on four public finger vein databases show that the proposed method realizes efficient transfer and achieves recognition performance as good as the best end-to-end training method dose in the target task. For common application scenarios, the proposed method can meet the requirements of real-time deployment and provide a feasible solution for the application of deep learning techniques in cross-dataset finger vein recognition.
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