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Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.

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Replacing normal convolutions with group convolutions can significantly increase the computational efficiency of modern deep convolutional networks, which has been widely adopted in compact network architecture designs. However, existing group convolutions undermine the original network structures by cutting off some connections permanently resulting in significant accuracy degradation. In this paper, we propose dynamic group convolution (DGC) that adaptively selects which part of input channels to be connected within each group for individual samples on the fly. Specifically, we equip each group with a small feature selector to automatically select the most important input channels conditioned on the input images. Multiple groups can adaptively capture abundant and complementary visual/semantic features for each input image. The DGC preserves the original network structure and has similar computational efficiency as the conventional group convolution simultaneously. Extensive experiments on multiple image classification benchmarks including CIFAR-10, CIFAR-100 and ImageNet demonstrate its superiority over the existing group convolution techniques and dynamic execution methods 4 . The code is available at https://github.com/zhuogege1943/dgc.

In this paper, a pose-invariant hand shape recognition method based on the geometry of the fingers is proposed. Firstly, inspired by the segmentation method presented by Yoruk et al., we conduct a novel improvement on the segmentation for extracting the region of the fingers when the hand is in a natural pose. Secondly, Fourier descriptors and finger area functions are employed to extract the finger boundary curve features and region areas, respectively. Finally, score-level fusion based on a weighted sum is used to obtain matching results. Because the finger segmentation strategy and the feature extraction method are both rotation and translation invariant, the proposed method is more suitable for a naturally posed hand. Experiments using the Bogazici University Hand database show that the proposed method can achieve an equal error rate of 0.0369 for all data and 0.0273 for samples with an intragroup angle deviation of less than 45 • . Thus, the proposed method is suitable for real-world applications.