论文标题
Geoconv：面部动作单位识别的测地卷卷积
GeoConv: Geodesic Guided Convolution for Facial Action Unit Recognition
论文作者
论文摘要
自动面部动作部门（AU）的识别引起了极大的关注，但仍然是一项艰巨的任务，因为当地面部肌肉的细微变化很难彻底捕获。大多数现有的AU识别方法都以直接的2D或3D方式利用几何信息，该信息忽略了3D多种多样的信息或遭受高计算成本的损失。在本文中，我们提出了一种新型的大地测量引导卷积（GEOCONV），以通过将3D歧管信息嵌入2D卷积中，以识别AU的识别。具体而言，Geoconv的内核是由我们引入的地球称重加权的，这些重量与重建的3D面部模型上的地理距离呈负相关。此外，基于GeoConv，我们进一步开发了一个端到端的可训练框架，名为Geocnn，以识别AU。关于BP4D和DISFA基准测试的广泛实验表明，我们的方法显着优于最先进的AU识别方法。
Automatic facial action unit (AU) recognition has attracted great attention but still remains a challenging task, as subtle changes of local facial muscles are difficult to thoroughly capture. Most existing AU recognition approaches leverage geometry information in a straightforward 2D or 3D manner, which either ignore 3D manifold information or suffer from high computational costs. In this paper, we propose a novel geodesic guided convolution (GeoConv) for AU recognition by embedding 3D manifold information into 2D convolutions. Specifically, the kernel of GeoConv is weighted by our introduced geodesic weights, which are negatively correlated to geodesic distances on a coarsely reconstructed 3D face model. Moreover, based on GeoConv, we further develop an end-to-end trainable framework named GeoCNN for AU recognition. Extensive experiments on BP4D and DISFA benchmarks show that our approach significantly outperforms the state-of-the-art AU recognition methods.
