Deep Learning based Automatic Facial Emotion Recognition - Srivastav, Shivangi; Khare, Ashish (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Automatic feeling acknowledgment reliant upon look is a captivating assessment field, which has presented and applied in a couple of districts like security, prosperity and in human machine interfaces. The task of video synopsis has been playing a significant role in domain of video surveillance based systems. Various automation-based system relies on the identification of human emotions exhibited in running video frames. In this work we have investigated the methods to enhance the performance of deep learning model for the task of facial emotion recognition. Models are trained on Facial Emotion Recognition (FER) database with a keen focus to identify the optimal learning parameters using fit one cycle policy. Transfer learning is applied on popular models viz VGG and Resnet to identify the delineating decision boundary for human emotions. The model has successfully achieved a permissible classification rate of 70.2 % on FER dataset respectively