Facial Expressions Recognitions with Supervised Contrastive Learning
DOI:
https://doi.org/10.54097/hset.v41i.6743Keywords:
Facial Expression Recognition; Convultional Neural Networks; Supervised Contrastive Learning.Abstract
Facial expressions are significant ways that humans express and perceive emotions. Facial Expression Recognition (FER) can be challenging in computer vision since facial expressions tend to be different among individuals. Researchers have introduced many outstanding methodologies and approaches to achieve the best performance in classifying and identifying emotions based on images of facial expressions. Supervised Contrastive Learning (SCL), outperforming supervised learning with cross-entropy loss, has gained much attention due to its recent performance in computer vision. In this work, we extended traditional approaches to common cross entropy loss and made approaches of contrastive learning to tackle the issue. Specifically, the frameworks in this work focus on the performance of cross entropy loss and supervised contrastive loss on the FER-2013 dataset. The result showed that supervised contrastive loss substantially improved accuracy by about 2% to 6% for ResNet architectures.
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