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Found 2 papers, 2 papers with code
Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model
In this paper, we tackle the issue of data imbalance by incorporating synthetic data augmentation and leveraging the ResEmoteNet model to enhance the overall performance on facial emotion recognition task.
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks.
Ranked #1 on
Facial Expression Recognition (FER)
on FER2013
(using extra training data)
Facial Emotion Recognition
Facial Expression Recognition (FER)
