To overcome the performance degradation of palm vein recognition system due to the small quantity and the uneven quality of palm vein images, a palm vein image recognition method based on side chain connected convolutional neural network was proposed. Firstly, palm vein features were extracted by convolution layer and pooling layer based on ResNet model. Secondly, the Exponential Linear Unit (ELU) activation function, Batch Normalization (BN) and Dropout technology were used to improve and optimize the model, so as to alleviate gradient disappear, prevent over fitting, speed up convergence and enhance the generalization ability of the model. Finally, Densely Connected Network (DenseNet) was introduced to make the extracted palm vein features more abundant and effective. Experimental results on two public databases and one self-built database show that, the recognition rates of the proposed method on the three databases are 99.98%, 97.95%, 97.96% respectively, indicating that the proposed method can effectively improve the performance of palm vein recognition system, and is more suitable for the practical applications of palm vein recognition.

To solve the degradation of recognition performance caused by unclear palm vein contour, low image contrast and brightness, a new palm vein enhancement method based on adaptive fusion was proposed. Firstly, based on Dark Channel Prior (DCP) defogging algorithm and adaptively selected defogging coefficient according to variation coefficient of the palm vein image, DCP enhanced image was obtained. And based on Partial Overlapped Sub-block Histogram Equalization (POSHE) algorithm, POSHE enhanced image was obtained. Secondly, the image was divided into 16 sub-blocks, and the weight of each sub-block was determined by the gray mean and the standard deviation. Finally, two kinds of enhanced images were fused adaptively according to the weight of each sub-block, obtaining the adaptive fused enhanced image. This method not only retains the advantages of DCP algorithm in enhancing image contrast and brightness without introducing significant noise, but also preserves the benefits of POSHE algorithm in enhancing image contrast and brightness without losing local details. Meanwhile, adaptive fusion of the two algorithms solves the problem of missing palm vein in shadow areas of DCP images and reduces the blocking artifacts produced by POSHE. Experimental results carried out on two public databases and a self-built database show that the equal error rates are 0.0004, 0.0472, 0.0579 and the correct recognition rates are 99.98%, 94.27%, 92.05% respectively, indicating that compared with existing image enhancement methods, the proposed method reduces the equal error rate and improves the recognition accuracy.