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.
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诊断出红斑的偏头膜（EM）皮肤病变，使用深度学习技术的莱姆病最常见的早期症状可以有效预防长期并发症。现有的基于深度学习的EM识别的作品仅由于缺乏与相关患者数据相关的莱姆病相关图像的数据集，因此仅利用病变图像。医师依靠患者有关皮肤病变背景的信息来确认其诊断。为了协助深度学习模型，根据患者数据计算出的概率分数，这项研究引起了15位医生的意见。对于启发过程，准备了一份与EM相关的问题和可能的答案的问卷。医生为问题的不同答案提供了相对权重。我们使用基于高斯混合物的密度估计将医生评估转换为概率得分。为了引起概率模型验证，我们利用了形式的概念分析和决策树。引起的概率得分可用于使基于图像的深度学习莱姆病预扫描剂稳健。
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