分层级联生成对抗网络用于手背静脉图像修复

为解决在识别过程中因手背静脉图像信息缺失而造成识别效率低下的问题，本文提出了分层级联生成对抗网络的手背静脉图像修复框架。该网络框架分别以级联与并行分层的方式进行修复操作，通过并行分层结构创新性的融合了不同静脉图像的特征信息；为有效地利用静脉图像的上下文信息对缺失的静脉图像信息进行预测与补全，在网络中创新性的引入了空洞卷积核与非局部注意力网络；为保证修复静脉图像质量与其真实图像的一致性，创新性的结合对抗损失与感知损失进行优化。实验结果表明，本文算法在视觉效果、峰值信噪比（Peak Signal to Noise Ratio，PSNR）和结构相似性（Structural Similarity Index，SSIM）等方面表现优于已有算法，并在两个公开的掌纹与指纹数据集上进行了有效的泛化验证。此外，修复图像相较于缺失图像在身份识别效率方面有了一定的提高。 

Hand-dorsal Vein Image Inpainting Using Hierarchical Cascade Generative Adversarial Networks

Despite being investigated more and more, it is still challenging to construct a reliable hand-dorsa vein recognition system. One of the challenges is about designing models to recognize hand-dorsa vein pattern precisely even when the images suffer from severe information lost. To address this and formulate a more robust vein recognition model, a hierarchical incomplete vein image inpainting framework is proposed as a cascaded two-stage network. In stage-I, an encoder-decoder network is designed using the dilated convolutional kernel and non-local attention design for better exploiting the contextual information. The coarse prediction from stage-I is then refined with a novel multi-branch encoder-decoder network. By taking the coarse prediction, incomplete vein image, and the binary segmentation map as inputs, the three encoder branches produce complementary semantic features, which are then combined via concatenation to obtain a better semantic representation for the decoder to produce refined images with higher quality. To stabilize the training of the cascaded network, both the adversarial loss and perceptual loss are combined, and two discriminators in terms of global and local design are proposed to guarantee smooth details inpainting. Rigorous experiments are carried out to evaluate the usefulness of the proposed model for the challenging vein image inpainting tasks. On the one hand, both qualitative and quantitative evaluation is performed on hand-dorsa vein image and palmprint image inpainting task. On the other hand, the improvement of recognition results on the specifically-created incomplete vein dataset after inpainting further demonstrates the effectiveness of our inpainting framework.