Facial Expression Recognition On Fer 1

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Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
PAtt-Lite	95.55	PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition
GReFEL	93.09	GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution
QCS	91.85	QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
ResNet18 Dense Architecture	91.41	Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond
DDAMFN	90.74	A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition
KTN	90.49	Adaptively Learning Facial Expression Representation via C-F Labels and Distillation
Vit-base + MAE	90.18	Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
FER-VT	90.04	Facial expression recognition with grid-wise attention and visual transformer
EAC	89.64	Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
LResNet50E-IR	89.257	Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition
ViT-base	88.91	Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
ViT-tiny	88.56	Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
Local Learning Deep + BOW	87.76	Local Learning with Deep and Handcrafted Features for Facial Expression Recognition
Ensemble with Shared Representations (ESR-9)	87.15	Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks

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Facial Expression Recognition On Fer 1 | SOTA | HyperAI