Facial Emotion Recognition: A multi-task approach using deep learning
This work addresses the challenge of facial emotion recognition for applications in human-computer interaction, though it appears incremental as it builds on existing multi-task learning approaches.
The paper tackles the problem of facial emotion recognition by proposing a multi-task learning algorithm that simultaneously detects gender, age, race, and emotion using a single CNN, achieving results significantly better than current state-of-the-art algorithms.
Facial Emotion Recognition is an inherently difficult problem, due to vast differences in facial structures of individuals and ambiguity in the emotion displayed by a person. Recently, a lot of work is being done in the field of Facial Emotion Recognition, and the performance of the CNNs for this task has been inferior compared to the results achieved by CNNs in other fields like Object detection, Facial recognition etc. In this paper, we propose a multi-task learning algorithm, in which a single CNN detects gender, age and race of the subject along with their emotion. We validate this proposed methodology using two datasets containing real-world images. The results show that this approach is significantly better than the current State of the art algorithms for this task.