Facial Expression Recognition on CK+
100AccuracyFER-VT
Evaluation Results
| Method | Links | |
|---|---|---|
| FER-VT2023.06 | 100 | |
| PAtt-Lite2023.06 | 100 | |
| PCNN2025.12 | 100 | |
| ViT + SE2023.06 | 99.8 | |
| FDRL2021.04 | 99.54 | |
| FDRL2023.06 | 99.54 | |
| FDRL2025.12 | 99.54 | |
| Proposed dynamic MTLlearning_type=multi-task, weighting=proposed dynamic2019.11 | 99.5 | |
| Multi-scale ViT and contrastive learningMethodology=Multi-scale ViT and contrastive learning2026.05 | 99.5 | |
| DDLNumber of expression categories=72021.04 | 99.16 | |
| Static MTLlearning_type=multi-task, weighting=static2019.11 | 99.11 | |
| Naive dynamic MTLlearning_type=multi-task, weighting=naive dynamic2019.11 | 99.1 | |
| FN2ENNumber of expression categories=62021.04 | 98.6 | |
| Compact CNN2025.12 | 98.47 | |
| Single-tasklearning_type=single-task2019.11 | 98.21 | |
| FMPNSetting=image-based2019.02 | 98.06 | |
| IF-GAN2023.06 | 97.52 | |
| DeRL2021.04 | 97.37 | |
| SCAN-CCI2023.06 | 97.31 | |
| DTAGN2019.11 | 97.3 | |
| PPDN2019.11 | 97.3 | |
| DeRFSetting=image-based2019.02 | 97.3 | |
| PPDNNumber of expression categories=62021.04 | 97.3 | |
| DTAGN(Joint)Setting=sequence-based2019.02 | 97.25 | |
| Deep network with joint fine-tuningClasses=72023.08 | 97.25 | |
| BaselineNumber of expression categories=72021.04 | 97.15 | |
| SCAN2025.12 | 97.07 | |
| pACNN2023.06 | 97.03 | |
| pACNN2025.12 | 97.03 | |
| Deep neural network using multi-step pre-processing and feature extractionClasses=72023.08 | 96.8 | |
| MSFERNetTrainable Params.=2.37M2026.05 | 96.77 | |
| Boosted deep belief networkClasses=72023.08 | 96.7 | |
| gACNN2023.06 | 96.4 | |
| gACNN2025.12 | 96.4 | |
| Manual feature extraction using Local Binary PatternsClasses=72023.08 | 96.26 | |
| DLP-CNNNumber of expression categories=62021.04 | 95.78 | |
| DLP-CNN2025.12 | 95.78 | |
| IACNNSetting=image-based2019.02 | 95.37 | |
| IACNN2021.04 | 95.37 | |
| Aikyn et al.Features=Geometric2025.12 | 95.12 | |
| LBPSVM2019.11 | 95.1 | |
| EfficientNet+XGBoostTrainable Params.=5.30M2026.05 | 94.41 | |
| STM-ExpletSetting=sequence-based2019.02 | 94.19 | |
| Inception2019.11 | 93.2 | |
| Deep learning using CNN and inception blocksClasses=72023.08 | 93.2 | |
| ConvLSTM1D-based Facial Expression RecognitionFeatures=Geometric2025.12 | 93 | |
| Choi et al. (2D LFM)Features=Geometric2025.12 | 92.6 | |
| IPA2LTNumber of expression categories=72021.04 | 92.45 | |
| Jung et al. (DTGN)Features=Geometric2025.12 | 92.35 | |
| AUDN2019.11 | 92.1 | |
| Mollahosseini et al. Proposed ModelTrainable Params.=24.17M2026.05 | 92.08 | |
| Qiu et al. (multiple-origin)Features=Geometric2025.12 | 92 | |
| CNN (no lg)Setting=image-based2019.02 | 91.82 | |
| AlexNetTrainable Params.=62.30M2026.05 | 91.68 | |
| HOG 3DSetting=sequence-based2019.02 | 91.44 | |
| VGGNetTrainable Params.=84.00M2026.05 | 91.39 | |
| CNN (baseline)Setting=image-based2019.02 | 90.78 | |
| Raj et al.Features=Geometric2025.12 | 89 | |
| Álvarez et al.Features=Geometric2025.12 | 89 | |
| LBP-TOPSetting=sequence-based2019.02 | 88.99 | |
| FTDNNSource set=Six datasets, Backbone=VGGNet2020.08 | 88.58 | |
| ECANSource set=RAF-DB 2.0, Backbone=VGGNet2020.08 | 86.49 | |
| AGRASource set=RAF-DB, Backbone=ResNet-502020.08 | 85.27 | |
| ICIDSource set=RAF-DB, Backbone=DarkNet-192020.08 | 84.5 | |
| ECANSource set=RAF-DB, Backbone=ResNet-502020.08 | 79.77 | |
| JUMBOTSource set=RAF-DB, Backbone=ResNet-502020.08 | 79.46 | |
| FTDNNSource set=RAF-DB, Backbone=ResNet-502020.08 | 79.07 | |
| DETNSource set=RAF-DB, Backbone=Manually designed network2020.08 | 78.83 | |
| DETNSource set=RAF-DB, Backbone=ResNet-502020.08 | 78.22 | |
| PLFTSource set=RAF-DB, Backbone=ResNet-502020.08 | 77.52 | |
| ICIDSource set=MMI, Backbone=DarkNet-192020.08 | 76.1 | |
| SAFNSource set=RAF-DB, Backbone=ResNet-502020.08 | 75.97 | |
| SWDSource set=RAF-DB, Backbone=ResNet-502020.08 | 75.19 | |
| ETDSource set=RAF-DB, Backbone=ResNet-502020.08 | 75.16 | |
| ICIDSource set=RAF-DB, Backbone=ResNet-502020.08 | 74.42 | |
| LPLSource set=RAF-DB, Backbone=ResNet-502020.08 | 74.42 | |
| STCNNSource set=MMI&FERA, Backbone=Inception-ResNet2020.08 | 73.91 | |
| CADASource set=RAF-DB, Backbone=ResNet-502020.08 | 72.09 | |
| DTSource set=RAF-DB, Backbone=ResNet-502020.08 | 71.32 | |
| E3DCNNSource set=MMI&FERA&DISFA, Backbone=Inception-ResNet2020.08 | 67.52 | |
| DFASource set=RAF-DB, Backbone=ResNet-502020.08 | 64.26 | |
| GDFERSource set=Six datasets, Backbone=Inception2020.08 | 64.2 | |
| Da et al.Source set=BOSPHORUS, Backbone=HOG & Gabor filters2020.08 | 57.6 |