Despite being the appearance-based classifier of choice in recent years,
relatively few works have examined how much convolutional neural networks
(CNNs) can improve performance on accepted expression recognition benchmarks
and, more importantly, examine what it is they actually learn. In this work,
not only do we show that CNNs can achieve strong performance, but we also
introduce an approach to decipher which portions of the face influence the
CNN's predictions. First, we train a zero-bias CNN on facial expression data
and achieve, to our knowledge, state-of-the-art performance on two expression
recognition benchmarks: the extended Cohn-Kanade (CK+) dataset and the Toronto
Face Dataset (TFD). We then qualitatively analyze the network by visualizing
the spatial patterns that maximally excite different neurons in the
convolutional layers and show how they resemble Facial Action Units (FAUs).
Finally, we use the FAU labels provided in the CK+ dataset to verify that the
FAUs observed in our filter visualizations indeed align with the subject's
facial movements.

在面部表情数据上使用零偏差卷积神经网络，作者在扩展的 Cohn-Kanade 和多伦多人脸数据集上取得了领先水平的性能，并使用空间模式可视化的方法解释了这些卷积神经网络是如何进行表情识别并关联面部动作元素的。