Comparison of CNN Architecture for Thermal Face Emotion Classification

After the corona pandemic, people's feelings of depression are increasing in most countries. Emotion recognition is an essential technology for the increasing demand for non-face-to-face medical services to reduce the risk of infection. A skin temperature pattern can be used as one of the indicators indicating the emotional state. In this paper, the self-constructed thermal image DB was applied to the existing CNN architecture for facial expression classification and performance evaluation was performed. As a result, it was confirmed that the network in which several kernels are applied in parallel in order to extract various features has good performance.