Emotion Recognition Using Deep Learning in Pandemic with Real-time Email Alert

Emotion recognition is considered as one of the most vital and challenging studies in the domain of computer vision. Due to the ongoing pandemic the people’s emotional and mental state is getting affected due to less physical interaction and increased virtual interaction. The motivation of the work lies in the recognition and monitoring of the emotional state of the human beings in live environment with higher accuracy rate and fast recognition time so as to keep them aware about their emotional state in the pandemic situation. Emotion recognition is achieved using the proposed deep convolution neural network (DCNN) using custom Gabor filter with 85.8% accuracy. Emotion recognition is also being investigated and compared with other deep learning models such as AlexNet, VGG-Net for testing the efficacy. When the emotion of the person will be found sad or angry then an alert sound is played and an email alert is automatically sent.