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ResearchFormerly WizWand

Facial Expression Recognition on CK+

100AccuracyFER-VT
55.90467.35278.890.248Feb 23, 2019May 10, 2020Jul 26, 2021Oct 12, 2022Dec 28, 2023Mar 14, 2025May 31, 2026
Updated 4mo ago

Evaluation results

MethodLinks
FER-VT2023.06100
PAtt-Lite2023.06100
PCNN2025.12100
ViT + SE2023.0699.8
FDRL2021.0499.54
FDRL2023.0699.54
FDRL2025.1299.54
Proposed dynamic MTLlearning_type=multi-task, weighting=proposed dynamic2019.1199.5
Multi-scale ViT and contrastive learningMethodology=Multi-scale ViT and contrastive learning2026.0599.5
DDLNumber of expression categories=72021.0499.16
Static MTLlearning_type=multi-task, weighting=static2019.1199.11
Naive dynamic MTLlearning_type=multi-task, weighting=naive dynamic2019.1199.1
FN2ENNumber of expression categories=62021.0498.6
Compact CNN2025.1298.47
Single-tasklearning_type=single-task2019.1198.21
FMPNSetting=image-based2019.0298.06
IF-GAN2023.0697.52
DeRL2021.0497.37
SCAN-CCI2023.0697.31
DTAGN2019.1197.3
PPDN2019.1197.3
DeRFSetting=image-based2019.0297.3
PPDNNumber of expression categories=62021.0497.3
DTAGN(Joint)Setting=sequence-based2019.0297.25
Deep network with joint fine-tuningClasses=72023.0897.25
BaselineNumber of expression categories=72021.0497.15
SCAN2025.1297.07
pACNN2023.0697.03
pACNN2025.1297.03
Deep neural network using multi-step pre-processing and feature extractionClasses=72023.0896.8
MSFERNetTrainable Params.=2.37M2026.0596.77
Boosted deep belief networkClasses=72023.0896.7
gACNN2023.0696.4
gACNN2025.1296.4
Manual feature extraction using Local Binary PatternsClasses=72023.0896.26
DLP-CNNNumber of expression categories=62021.0495.78
DLP-CNN2025.1295.78
IACNNSetting=image-based2019.0295.37
IACNN2021.0495.37
Aikyn et al.Features=Geometric2025.1295.12
LBPSVM2019.1195.1
EfficientNet+XGBoostTrainable Params.=5.30M2026.0594.41
STM-ExpletSetting=sequence-based2019.0294.19
Inception2019.1193.2
Deep learning using CNN and inception blocksClasses=72023.0893.2
ConvLSTM1D-based Facial Expression RecognitionFeatures=Geometric2025.1293
Choi et al. (2D LFM)Features=Geometric2025.1292.6
IPA2LTNumber of expression categories=72021.0492.45
Jung et al. (DTGN)Features=Geometric2025.1292.35
AUDN2019.1192.1
Mollahosseini et al. Proposed ModelTrainable Params.=24.17M2026.0592.08
Qiu et al. (multiple-origin)Features=Geometric2025.1292
CNN (no lg)Setting=image-based2019.0291.82
AlexNetTrainable Params.=62.30M2026.0591.68
HOG 3DSetting=sequence-based2019.0291.44
VGGNetTrainable Params.=84.00M2026.0591.39
CNN (baseline)Setting=image-based2019.0290.78
Raj et al.Features=Geometric2025.1289
Álvarez et al.Features=Geometric2025.1289
LBP-TOPSetting=sequence-based2019.0288.99
FTDNNSource set=Six datasets, Backbone=VGGNet2020.0888.58
ECANSource set=RAF-DB 2.0, Backbone=VGGNet2020.0886.49
AGRASource set=RAF-DB, Backbone=ResNet-502020.0885.27
ICIDSource set=RAF-DB, Backbone=DarkNet-192020.0884.5
ECANSource set=RAF-DB, Backbone=ResNet-502020.0879.77
JUMBOTSource set=RAF-DB, Backbone=ResNet-502020.0879.46
FTDNNSource set=RAF-DB, Backbone=ResNet-502020.0879.07
DETNSource set=RAF-DB, Backbone=Manually designed network2020.0878.83
DETNSource set=RAF-DB, Backbone=ResNet-502020.0878.22
PLFTSource set=RAF-DB, Backbone=ResNet-502020.0877.52
ICIDSource set=MMI, Backbone=DarkNet-192020.0876.1
SAFNSource set=RAF-DB, Backbone=ResNet-502020.0875.97
SWDSource set=RAF-DB, Backbone=ResNet-502020.0875.19
ETDSource set=RAF-DB, Backbone=ResNet-502020.0875.16
ICIDSource set=RAF-DB, Backbone=ResNet-502020.0874.42
LPLSource set=RAF-DB, Backbone=ResNet-502020.0874.42
STCNNSource set=MMI&FERA, Backbone=Inception-ResNet2020.0873.91
CADASource set=RAF-DB, Backbone=ResNet-502020.0872.09
DTSource set=RAF-DB, Backbone=ResNet-502020.0871.32
E3DCNNSource set=MMI&FERA&DISFA, Backbone=Inception-ResNet2020.0867.52
DFASource set=RAF-DB, Backbone=ResNet-502020.0864.26
GDFERSource set=Six datasets, Backbone=Inception2020.0864.2
Da et al.Source set=BOSPHORUS, Backbone=HOG & Gabor filters2020.0857.6