Facial Emotion Recognition: A multi-task approach using deep learning
원문 초록
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.
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Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction
Joint Training on Multiple Datasets With Inconsistent Labeling Criteria for Facial Expression Recognition
DetFusion: A Detection-driven Infrared and Visible Image Fusion Network
토론
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