论文标题
用于面部姿势归一化和情感识别的生成对抗性堆叠的自动编码器
Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition
论文作者
论文摘要
在这项工作中，我们提出了一种新颖的生成对抗堆叠自动编码器，该自动编码器学会映射面部表情，最高或负60度，以照明不变的面部表现为0度。我们通过使用利用局部和全局空间信息的新型卷积层以及具有减少利用面部对称性的参数数量的卷积层来实现这一目标。此外，我们引入了一种生成的对抗性渐进式贪婪的层面学习算法，该算法旨在以有效而增量的方式训练对抗性自动编码器。我们证明了我们方法的效率，并报告了几种面部情感识别语料库的最先进的表现，其中包括在野外收集的一个。
In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions, with up to plus or minus 60 degrees, to an illumination invariant facial representation of 0 degrees. We accomplish this by using a novel convolutional layer that exploits both local and global spatial information, and a convolutional layer with a reduced number of parameters that exploits facial symmetry. Furthermore, we introduce a generative adversarial gradual greedy layer-wise learning algorithm designed to train Adversarial Autoencoders in an efficient and incremental manner. We demonstrate the efficiency of our method and report state-of-the-art performance on several facial emotion recognition corpora, including one collected in the wild.
