Abstract
Due to the advantages of lightweight networks such as efficiency, portability, and fast inference, we propose a palm vein recognition network based on lightweight neural networks. The network consists of three stages combining Blocks of ShuffleNetV2, Blocks of MobileNetV3, and MBConv of EfficientNet. The number of each stage in the network is chosen based on the baseline parameters and computational effort, and the expansion factors are re-analyzed and selected. The performance of the proposed network is compared to the baseline with a 16.78% reduction in error rate and a 10.91% compression of parameters. The experimental results show that the proposed palm vein recognition network is effective, which can potentially contribute to the development of more accurate, portable and reliable biometric systems.
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