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Convolutional neural networks (CNNs) were popular in ImageNet large scale visual recognition competition (ILSVRC 2012) because of their identification ability and computational efficiency. This paper proposes a palm vein recognition method based on CNN. The four main steps of palm vein recognition are image acquisition, image preprocessing, feature extraction, and matching. To reduce the processing steps in the recognition of palm vein images, a palm vein recognition method using a CNN is proposed. CNN is a deep learning network. Palm vein images are acquired using near-infrared light, under which the veins in the palm of the hand are relatively prominent. To obtain a good vein image, many previous methods used preprocessing to further enhance the image before using feature extraction to find feature matches for further comparison. In recent years, CNNs have been shown to have great advantages and have performed well in image classification. To reduce early-stage image processing, a CNN is used to classify and recognize palm vein images. The networks AlexNet and VGG depth CNN were trained to extract image features. The palm vein recognition rates by VGG-19, VGG-16, and AlexNet were 98.5%, 97.5%, and 96%, respectively. [ABSTRACT FROM AUTHOR]