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Facial Expression Recognition On Fer2013

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
Segmentation VGG-19	75.97	A novel facial emotion recognition model using segmentation VGG-19 architecture
Ensemble ResMaskingNet with 6 other CNNs	76.82	Facial Expression Recognition using Residual Masking Network
CNN Hyperparameter Optimisation	72.16	Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition
ResEmoteNet	79.79	ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
Ad-Corre	72.03	Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild	-
Mini-ResEmoteNet (B)	70.20	Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design	-
Local Learning Deep+BOW	75.42	Local Learning with Deep and Handcrafted Features for Facial Expression Recognition	-
LHC-Net	74.42	Local Multi-Head Channel Self-Attention for Facial Expression Recognition
EmoNeXt	76.12	EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
Mini-ResEmoteNet (A)	76.33	Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design	-
Regularized Xception with Step Decay Learning	94.34	Regularized Xception for facial expression recognition with extra training data and step decay learning rate
ResNet18 With Tricks	73.70	Fer2013 Recognition - ResNet18 With Tricks
Local Learning BOW	67.48	Challenges in Representation Learning: A report on three machine learning contests
VGGNet	73.28	Facial Emotion Recognition: State of the Art Performance on FER2013
Residual Masking Network	74.14	Facial Expression Recognition using Residual Masking Network
DeepEmotion	70.02	Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

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