Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion

A web application with real-time emotion recognition for psychologists and psychiatrists is presented. Mental health effects during COVID-19 quarantine need to be handled because society is being emotionally impacted. The human micro-expressions can describe genuine emotions that can be captured by Convolutional Neural Networks (CNN) models. But the challenge is to implement it under the poor performance of a part of society computers and the low speed of internet connection, i.e., improve the computational efficiency and reduce the data transfer. To validate the computational efficiency premise, we compare CNN architectures results, collecting the floating-point operations per second (FLOPS), the Number of Parameters (NP) and accuracy from the MobileNet, PeleeNet, Extended Deep Neural Network (EDNN), Inception- Based Deep Neural Network (IDNN) and our proposed Residual mobile-based Network model (ResmoNet). Also, we compare the trained models results in terms of Main Memory Utilization (MMU) and Response Time to complete the Emotion (RTE) recognition. Besides, we design a data transfer that includes the raw data of emotions and the basic patient information. The web application was evaluated with the System Usability Scale (SUS) and a utility questionnaire by psychologists and psychiatrists. ResmoNet model generated the most reduced NP, FLOPS, and MMU results, only EDNN overcomes ResmoNet in 0.01sec in RTE. The optimizations to our model impacted the accuracy, therefore IDNN and EDNN are 0.02 and 0.05 more accurate than our model respectively. Finally, according to psychologists and psychiatrists, the web application has good usability (73.8 of 100) and utility (3.94 of 5).