论文标题
面部紫外线图的完成姿势不变的面部识别：一种基于耦合的剩余UNET的新型对抗方法
Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets
论文作者
论文摘要
姿势不变的面部识别是指通过分析从不同姿势捕获的面部图像来识别或验证人的问题。由于姿势，照明和面部表达的差异很大，这个问题具有挑战性。处理姿势变化的一种有希望的方法是实现从野外面提取的不完整的紫外线图，然后将完成的UV地图附加到拟合的3D网格上，最后生成不同的2D姿势面孔。合成的面板增加了训练深面识别模型的姿势变化，并在测试阶段减少姿势差异。在本文中，我们提出了一种名为“注意救援gan”的新型生成模型，以改善紫外线图的完成。我们通过使用几个U网络来增强原始的UV-GAN。特别是，每个U-NET内的跳过连接都会通过注意力门来提高。同时，两个U-NET的功能与可训练的标量重融合在一起。在流行基准测试的实验，包括多PIE，LFW，CPLWF和CFP数据集，表明，与其他现有方法相比，所提出的方法可以产生较高的性能。
Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses. The synthesized faces increase the pose variation for training deep face recognition models and reduce the pose discrepancy during the testing phase. In this paper, we propose a novel generative model called Attention ResCUNet-GAN to improve the UV map completion. We enhance the original UV-GAN by using a couple of U-Nets. Particularly, the skip connections within each U-Net are boosted by attention gates. Meanwhile, the features from two U-Nets are fused with trainable scalar weights. The experiments on the popular benchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that the proposed method yields superior performance compared to other existing methods.
