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 | EfficientFER | Accuracy | 82.47 | — | Unverified |
| 2 | FERNeXt-SDAFE | Accuracy | 81.33 | — | Unverified |
| 3 | ResEmoteNet | Accuracy | 79.79 | — | Unverified |
| 4 | Ensemble ResMaskingNet with 6 other CNNs | Accuracy | 76.82 | — | Unverified |
| 5 | Mini-ResEmoteNet (A) | Accuracy | 76.33 | — | Unverified |
| 6 | EmoNeXt | Accuracy | 76.12 | — | Unverified |
| 7 | Segmentation VGG-19 | Accuracy | 75.97 | — | Unverified |
| 8 | Local Learning Deep+BOW | Accuracy | 75.42 | — | Unverified |
| 9 | LHC-Net | Accuracy | 74.42 | — | Unverified |
| 10 | Residual Masking Network | Accuracy | 74.14 | — | Unverified |