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Table 1 Comparison of literatures

From: Facial expression recognition using optimized active regions

Databases Literatures Features Classifiers Accuracy (%)
CK+ Liu et al. [11]   DBDN 96.7
Shan et al. [21] LBP of weighted patch SVM 88.4
Happy et al. [23] LBP of active patch SVM 94.09
Lopes et al. [28]   CNN 96.76
Zhong et al. [35] LBP Multi-task sparse learning 89.89
Shan et al. [45] Boosting LBP SVM 92.6
Burkert et al. [47]   CNN 99.6
Ding et al. [49]   FaceNex2ExpNet 98.6
Zeng et al. [50] LBP, HOG, gray value DSAE 95.79
Zeng et al. [51]   LTNet 92.45
Chen et al. [52] HOG-TOP SVM 89.6
Kumari et al. [53] LBP, HOG, LDP K-Nearest Neighbor 97.96
JAFFE Liu et al. [11]   DBDN 91.8
Happy et al. [23] LBP of active patch SVM 92.63
Lopes et al. [28]   CNN 53.57
Shan et al. [45] Boosting LBP SVM 81
Kumari et al. [53] LBP, HOG, LDP K-Nearest Neighbor 95.31
MMI Zhong et al. [35] LBP Multi-task sparse learning 73.53
Shan et al. [45] Boosting LBP SVM 86.9
Burkert et al. [47]   CNN 98.63
Zeng et al. [51]   LTNet 65.61