Abstract
Benefiting from CNN’s strong feature expression ability, the finger vein recognition systems using the convolutional neural network (CNN) currently have shown a good performance. However, these systems usually adopt such large networks or complex step-by-step processes that they cannot be applied to the hardware platform with limited computing power and small memory. To address this limitation, this research proposes a finger vein recognition network based on difference image and 3C image for cascade fine-tuning. First, a difference image from the image pair for authentic matching or imposter matching is obtained by difference operation and a 2C image is acquired by regarding this image pair as a two-channel image; furthermore, a 3C image is gained with the channel connection of the difference image and 2C image. Then, the SqueezeNet (this network has been pre-trained on ImageNet) that receives the 3C image as input is fine-tuned and the best fine-tune manner is determined. Finally, a cascade fine-tune framework is designed to integrate the difference images and 3C image. In this paper, the size of SqueezeNet which is cascade fine-tuned on the basis of the pre-training weights is 5.63 MB, and the corresponding equal error rate(EER) acquired on the dataset MMCBNU_6000 and SDUMLA-HMT is 1.889% and 4.906% respectively. The experimental results fully prove that the proposed method achieves not only high recognition accuracy but also the simplification of network.
This work is supported by NNSF (No. 61771347), Characteristic Innovation Project of Guangdong Province (No. 2017KTSCX181), Young innovative talents project of Guangdong Province (2017KQNCX206), Jiangmen science and technology project ([2017] No. 268), Youth Foundation of Wuyi University (No. 2015zk11).
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Zeng, J. et al. (2019). A Novel Method for Finger Vein Recognition. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_6
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