Table 2 Comparison of results on different methods
From: Robust facial expression recognition system in higher poses
Method | Database | Recognition (%) | Ref |
|---|---|---|---|
Twin support vector machines classifier | MMI | \(92.56\pm 3.02\) | [32] |
DBM-DACNN with entropy loss | MMI | 79.25 | [33] |
Deep learning neural network-regression | CK + | 97.27 | [30] |
Deep learning + random forest | CK + | 99.00 | [31] |
Twin support vector machines classifier | CK + | \(93.42\pm 3.25\) | [32] |
DBM-DACNN with entropy loss | CK + | 96.46 | [33] |
Geotopo + | BP4D-Spontaneous | 88.56 | [34] |
Two-phase weighted collaborative representation classification | BP4D-Spontaneous | 100 | [35] |
Fine-grained matching of 3D keypoint descriptors | Bosphorus | 98.90 | [21] |
Kernel methods on Riemannian manifold | Bosphorus | 86.70 | [36] |
SVM with EPE | Bosphorus | 84.00 | [37] |
Two-phase weighted collaborative representation classification | Bosphorus | 98.90 | [35] |
Kernel methods on Riemannian manifold | BU-3DFE | 92.62 | [36] |
SVM with EPE | BU-3DFE | 85.81 | [37] |
Manifold CNN | BU-3DFE | 86.67 | [38] |
CNN model | BU-3DFE | 92.57 | [39] |
Proposed method | MMI | 97.20 | This study |
Proposed method | CK + | 98.20 | This study |
Proposed method | BP4D-Spontaneous | 97.20 | This study |
Proposed method | Bosphorus | 98.90 | This study |
Proposed method | BU-3DFE | 93.50 | This study |