Abstract
Over time for the past few years, facial expression identification has been a promising area. However, darkness, lighting conditions, and other factors make facial emotion identification challenging to detect. As a result, thermal images are suggested as a solution to such problems and for a variety of other benefits. Furthermore, focusing on significant regions of a face rather than the entire face is sufficient for reducing processing and improving accuracy at the same time. This research introduces novel infrared thermal image-based approaches for facial emotion recognition. First, the entire image of the face is separated into four pieces. Then, we accepted only four active regions (ARs) to prepare training and testing datasets. These four ARs are the left eye, right eye, and lips areas. In addition, ten-folded cross-validation is proposed to improve recognition accuracy using Convolutional Neural Network (CNN), a machine learning technique. Furthermore, we incorporated a parallelism technique to reduce processing-time in testing and training datasets. As a result, we have seen that the processing time reduces to 50%. Finally, a decision-level fusion is applied to improve the recognition accuracy. As a result, the proposed technique achieves a recognition accuracy of 96.87 %. The achieved accuracy ascertains the robustness of our proposed scheme.