论文标题
FaceChannel：一个轻巧的深度神经网络，用于面部表情识别
The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
论文作者
论文摘要
自动FER的当前最新模型基于难以训练的非常深的神经网络。这使得将这些模型适应不断变化的条件是一项挑战，这是FER模型的要求，鉴于情感感知和理解的主观性质。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。我们在不同的基准数据集上执行一系列实验，以证明与当前的FER相比，FaceChannel如何实现可比较的（即使不是更好的表现）。
Current state-of-the-art models for automatic FER are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing conditions, a requirement from FER models given the subjective nature of affect perception and understanding. 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 perform a series of experiments on different benchmark datasets to demonstrate how the FaceChannel achieves a comparable, if not better, performance, as compared to the current state-of-the-art in FER.
