  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).
