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 | ResEmoteNet | Accuracy (7 emotion) | 72.93 | — | Unverified |
| 2 | Norface | Accuracy (8 emotion) | 68.69 | — | Unverified |
| 3 | EmoAffectNet | Accuracy (7 emotion) | 66.49 | — | Unverified |
| 4 | Emotion-GCN | Accuracy (7 emotion) | 66.46 | — | Unverified |
| 5 | FaceBehaviorNet | Accuracy (7 emotion) | 65.4 | — | Unverified |
| 6 | Ada-DF | Accuracy (7 emotion) | 65.34 | — | Unverified |
| 7 | EAC | Accuracy (7 emotion) | 65.32 | — | Unverified |
| 8 | PAENet | Accuracy (7 emotion) | 65.29 | — | Unverified |
| 9 | DACL | Accuracy (7 emotion) | 65.2 | — | Unverified |
| 10 | DDAMFN++ | Accuracy (8 emotion) | 65.04 | — | Unverified |