ML Research Wiki / Benchmarks / Facial Expression Recognition / FER2013

FER2013

Facial Expression Recognition Benchmark

Performance Over Time

📊 Showing 3 results | 📏 Metric: Accuracy

Rank	Model	Paper	Accuracy	Date	Code
1	VGG	Facial Expression Recognition using Convolutional Neural Networks: State of the Art	72.70	2016-12-09	📦 amilkh/cs230-fer 📦 pranjalrai-iitd/FER2013-Facial-Emotion-Recognition- 📦 apuayush/face_express 📦 janZub-AI/EmotionDetectionVGG
2	Res-Net	Facial Expression Recognition using Convolutional Neural Networks: State of the Art	72.40	2016-12-09	📦 amilkh/cs230-fer 📦 pranjalrai-iitd/FER2013-Facial-Emotion-Recognition- 📦 apuayush/face_express 📦 janZub-AI/EmotionDetectionVGG
3	Inception	Facial Expression Recognition using Convolutional Neural Networks: State of the Art	71.60	2016-12-09	📦 amilkh/cs230-fer 📦 pranjalrai-iitd/FER2013-Facial-Emotion-Recognition- 📦 apuayush/face_express 📦 janZub-AI/EmotionDetectionVGG

2016

VGG

amilkh/cs230-fer pranjalrai-iitd/FER2013-Facial-Emotion-Recognition-

2016

Res-Net

amilkh/cs230-fer pranjalrai-iitd/FER2013-Facial-Emotion-Recognition-

2016

Inception

amilkh/cs230-fer pranjalrai-iitd/FER2013-Facial-Emotion-Recognition-

Model	Paper	Accuracy	Date
VGG	Facial Expression Recognition using Convolutional…	72.70	2016-12-09
Res-Net	Facial Expression Recognition using Convolutional…	72.40	2016-12-09
Inception	Facial Expression Recognition using Convolutional…	71.60	2016-12-09