论文标题
FaceChannel：速度与激情的深度神经网络，用于面部表情识别
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition
论文作者
论文摘要
自动面部表达识别（FER）的当前最新模型基于非常深的神经网络，这些网络有效但相当昂贵。考虑到FER的动态条件，这种特征阻碍了这种模型用作一般影响识别。在本文中，我们通过将FaceChannel正式化，这是一个轻巧的神经网络，该网络的参数比常见的深神经网络少得多。我们引入了一个抑制层，有助于在网络的最后一层中塑造面部特征的学习，从而改善性能，同时减少可训练的参数的数量。为了评估我们的模型，我们在不同的基准数据集上执行一系列实验，并演示FaceChannel如何与FER中当前最新的最新表现相当，甚至更好。我们的实验包括跨数据集分析，以估计我们的模型在不同的情感识别条件上的表现。我们通过分析FaceChannel如何学习并将学习的面部特征适应不同数据集的结论。
Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and thus improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapt the learned facial features towards the different datasets.
