Facial Expression Recognition (FER)
Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.
( Image credit: DeXpression )
Papers
Showing 1–10 of 492 papers
All datasetsAffectNetRAF-DBFER2013FER+Acted Facial Expressions In The Wild (AFEW)CK+FERPlusJAFFESFEWAff-Wild2BP4DDISFA
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ResNet50 | Accuracy(on validation set) | 65.5 | — | Unverified |
| 2 | LResNet50E-IR (5 models with augmentation) | Accuracy(on validation set) | 65.5 | — | Unverified |
| 3 | EAC | Accuracy(on validation set) | 65.32 | — | Unverified |
| 4 | LResNet50E-IR (1 model with augmentation) | Accuracy(on validation set) | 63.7 | — | Unverified |
| 5 | LResNet50E-IR (1 model) | Accuracy(on validation set) | 61.1 | — | Unverified |
| 6 | Multi-task EfficientNet-B0 | Accuracy(on validation set) | 59.27 | — | Unverified |
| 7 | resnet18_noisy | Accuracy(on validation set) | 55.17 | — | Unverified |
| 8 | resnet18 | Accuracy(on validation set) | 51.18 | — | Unverified |