JISE


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Journal of Information Science and Engineering, Vol. 37 No. 4, pp. 809-825


Design of a Lightweight Palmf-Vein Authentication System Based on Model Compression


ZIH-CHING CHEN1, SIN-YE JHONG2 AND CHIN-HSIEN HSIA2,3
1Department of Economics
National Taiwan University, Taipei, Taiwan

2Department of Engineering Science
National Cheng Kung University, Tainan, Taiwan

3Department of Computer Science and Information Engineering
National Ilan University, Yilan, Taiwan

E-mail: chhsia625@gmail.com


Palm-vein authentication is a secure and highly accurate vein feature authentication technology that has recently gained a lot of attention. Convolutional neural networks (CNNs) provide relatively high performance in the field of image processing, computer vision, and have been adapted for feature learning of palm-vein images. However, they often require high computation that not only are infeasible for real-time vein verification but also a challenge to apply on mobile devices. To address this limitation, we proposed a lightweight MobileNet based deep learning (DL) architecture with depthwise separable convolution (DSC) and adopt a knowledge distillation (KD) method to learn the knowledge from the more complex CNN, which makes it small but effective. Through the depth of separable convolution, the number of model parameters is significantly decreased, while still remaining high accuracy and stable performance. Experiments demonstrated that the size of the proposed model is 100 times less than the Inception_v3 model, while the performance can go beyond 98% correct identification rate (CIR) for the CASIA database.


Keywords: palm-vein recognition, knowledge distillation, lightweight, depthwise separable convolution, biometrics image

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