Research Article
Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine
Table 5
Comparison of ELM/RELM with the traditional classifiers on the CK dataset.
| Classifiers | AlexNet | DenseNet | GoogleNet | Inceptionv3 | ResNet101 | Accuracy | Precision | F1-score | Accuracy | Precision | F1-score | Accuracy | Precision | F1-score | Accuracy | Precision | F1-score | Accuracy | Precision | F1-score |
| RELM | 0.82 | 0.80 | 0.83 | 0.83 | 0.85 | 0.82 | 0.78 | 0.75 | 0.78 | 0.78 | 0.69 | 0.74 | 0.82 | 0.84 | 0.82 | ELM | 0.83 | 0.79 | 0.82 | 0.73 | 0.75 | 0.74 | 0.72 | 0.76 | 0.70 | 0.78 | 0.79 | 0.79 | 0.56 | 0.46 | 0.52 | SVM | 0.80 | 0.80 | 0.81 | 0.77 | 0.75 | 0.77 | 0.80 | 0.80 | 0.80 | 0.77 | 0.75 | 0.77 | 0.82 | 0.82 | 0.80 | Naïve Bayes | 0.23 | 0.25 | 0.25 | 0.20 | 0.28 | 0.25 | 0.29 | 0.28 | 0.28 | 0.24 | 0.25 | 0.24 | 0.43 | 0.45 | 0.45 | Random forest | 0.45 | 0.49 | 0.46 | 0.76 | 0.79 | 0.75 | 0.53 | 0.48 | 0.52 | 0.52 | 0.53 | 0.52 | 0.70 | 0.77 | 0.75 | K-nearest neighbors | 0.57 | 0.59 | 0.56 | 0.60 | 0.68 | 0.69 | 0.60 | 0.60 | 0.61 | 0.53 | 0.54 | 0.59 | 0.79 | 0.79 | 0.77 | Logistic regression | 0.29 | 0.31 | 0.29 | 0.53 | 0.52 | 0.52 | 0.55 | 0.59 | 0.56 | 0.58 | 0.60 | 0.59 | 0.59 | 0.60 | 0.61 | Random tree | 0.26 | 0.28 | 0.25 | 0.53 | 0.55 | 0.50 | 0.75 | 0.75 | 0.73 | 0.57 | 0.57 | 0.57 | 0.55 | 0.53 | 0.57 | Simple logistic | 0.48 | 0.47 | 0.48 | 0.73 | 0.74 | 0.70 | 0.73 | 0.75 | 0.75 | 0.75 | 0.78 | 0.75 | 0.74 | 0.74 | 0.74 | Decision table | 0.23 | 0.20 | 0.23 | 0.48 | 0.45 | 0.46 | 0.74 | 0.70 | 0.74 | 0.43 | 0.45 | 0.40 | 0.42 | 0.42 | 0.42 | Multiclass classifier | 0.57 | 0.62 | 0.60 | 0.65 | 0.67 | 0.67 | 0.68 | 0.74 | 0.70 | 0.74 | 0.74 | 0.74 | 0.70 | 0.74 | 0.70 | Multilayer perceptron | 0.43 | 0.43 | 0.41 | 0.65 | 0.70 | 0.69 | 0.78 | 0.68 | 0.67 | 0.68 | 0.65 | 0.66 | 0.65 | 0.68 | 0.68 |
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