Vein pattern recognition is a biometric identification technology that relies on the unique vascular patterns of each person’s hand. In this paper, we propose a method for people recognition that uses a combination of wavelet invariant moments and Convolutional Neural Networks (CNNs). Wavelet moments are a set of features extracted from an image using wavelet transform. These features can be invariant to translation, rotation, and scaling, which makes them well-suited for people recognition. CNNs have been shown to be very effective for image classification tasks. In this paper, the method is evaluated on 6000 palm vein images from the PolyU Multispectral Palmprint Database. The results show that the proposed method achieves an accuracy of 99%, which is higher than the accuracy of existing methods.
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