论文标题
2D+3D面部表达通过判别动态范围增强和多尺度学习
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning
论文作者
论文摘要
在2D+3D面部表达识别（FER）中，现有方法生成多视文几何图以增强深度特征表示。但是，这可能会由于不完整的点云中局部平面拟合而引起错误的估计。在本文中，我们从信息理论的角度提出了一种新型的地图生成技术，以增加与强人性格变化相比的3D表达差异。首先，我们检查HDR深度数据以提取区分动态范围$ r_ {dis} $，并将$ r_ {dis} $的熵最大化为全局最佳。然后，为防止过度增强引起的大变形，我们引入了深度失真约束，并将复杂性从$ O（kn^2）$降低到$ O（knτ）$。此外，受约束的优化被建模为有向无环图中的$ K $ EDGES最大权重问题，我们通过动态编程有效地解决了它。最后，我们还设计了一个有效的面部注意力结构，以自动找到多尺度学习的微妙判别面部零件，并使用建议的损失功能$ \ Mathcal {l} _ {fa} $训练它，而没有任何面部地标。不同数据集上的实验结果表明，该方法具有有效性，并且在FER精度和生成的地图的输出熵方面均优于最先进的2D+3D FER方法。
In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation. However, this may introduce false estimations due to local plane fitting from incomplete point clouds. In this paper, we propose a novel Map Generation technique from the viewpoint of information theory, to boost the slight 3D expression differences from strong personality variations. First, we examine the HDR depth data to extract the discriminative dynamic range $r_{dis}$, and maximize the entropy of $r_{dis}$ to a global optimum. Then, to prevent the large deformation caused by over-enhancement, we introduce a depth distortion constraint and reduce the complexity from $O(KN^2)$ to $O(KNτ)$. Furthermore, the constrained optimization is modeled as a $K$-edges maximum weight path problem in a directed acyclic graph, and we solve it efficiently via dynamic programming. Finally, we also design an efficient Facial Attention structure to automatically locate subtle discriminative facial parts for multi-scale learning, and train it with a proposed loss function $\mathcal{L}_{FA}$ without any facial landmarks. Experimental results on different datasets show that the proposed method is effective and outperforms the state-of-the-art 2D+3D FER methods in both FER accuracy and the output entropy of the generated maps.
