Abstract
At present, although the deep learning models represented by convolutional neural networks and Transformers have achieved promising recognition accuracies in finger vein (FV) recognition, there still remain some unresolved issues, including high model complexity and memory cost, as well as insufficient training samples. To address these issues, we propose an unsupervised spiking neural network for finger vein recognition (hereinafter dubbed ‘FV-SNN’), which utilizes Difference of Gaussian filter to encode the original image signal into a kind of spiking signal as input to the network, then, the FV-SNN model is trained in an unsupervised manner and the learned spiking features are fed to a LinearSVM classifier for final recognition. The experiments are performed on two benchmark FV datasets, and experimental results show that our proposed FV-SNN not only achieves competitive recognition accuracies, but also exhibits lower model complexity and faster training speed.
X. Xu–This work was supported by National Natural Science Foundation of China under Grant 62271130 and 62002053.
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Yang, L., Xu, X., Yao, Q. (2023). Finger Vein Recognition Based on Unsupervised Spiking Neural Network. 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_6
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DOI: https://doi.org/10.1007/978-981-99-8565-4_6
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