Abstract
This paper presents a new validation method using a convolutional neural network for palm vein recognition. Unlike fingerprint and face. Vein patterns are endogenous biometric features that do not change over time and that make them difficult to identify and replicate in people. The proposed paper aims to provide a new way to identify people through their veins. This paper used the CASIA dataset, which consists of several wavelengths, in this research used the 850nm wavelength, which is clear in the veins, In addition, we divided the data into 3 cases. The first case is when the training and testing ratio is 50/50, the second case when it is 70/30, and the last case when it is 90/10. Obtained an accuracy of 98% in the case 90/10. In addition, to the proposed network, and used a well-known global network, the AlexNet network, where did the same work on it to compare the results of our proposed network with it. As proposed network outperformed it in terms of accuracy and speed, where the accuracy was 96% in the case 90/10.
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