论文标题
使用面部表情的情绪识别方法的比较研究
A comparative study of emotion recognition methods using facial expressions
论文作者
论文摘要
了解对话者的面部表情对于丰富交流和使其深度超出明确表达的深度很重要。实际上，研究自己的面部表情可以洞悉其隐藏的情绪状态。但是，即使是人类，尽管我们对人类的情感经历有同情心和熟悉，但我们只能猜测对方的感觉。在人工智能和计算机视觉领域，面部情感识别（FER）是一个主题，主要是随着深度学习方法的进步和数据收集的改进而完全增长。本文的主要目的是比较三个FER数据集上的三个最先进网络的性能，每个网络都有自己的方法来改进FER任务。第一部分和第二部分分别描述了三个数据集和为FER任务设计的三个研究网络体系结构。在其余部分中概述了实验方案，结果及其解释。
Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden emotion state. However, even as humans, and despite our empathy and familiarity with the human emotional experience, we are only able to guess what the other might be feeling. In the fields of artificial intelligence and computer vision, Facial Emotion Recognition (FER) is a topic that is still in full growth mostly with the advancement of deep learning approaches and the improvement of data collection. The main purpose of this paper is to compare the performance of three state-of-the-art networks, each having their own approach to improve on FER tasks, on three FER datasets. The first and second sections respectively describe the three datasets and the three studied network architectures designed for an FER task. The experimental protocol, the results and their interpretation are outlined in the remaining sections.
