Table 5.
Performance evaluation of traditional machine learning models with different types of features.
| Features | Classifier | Accuracy | Precision | Recall | F1 Score | AUC |
|---|---|---|---|---|---|---|
| Skin | MLP | 66.02% | 66.26% | 65.64% | 64.47% | 65.64% |
| SVM | 65.95% | 69.42% | 65.95% | 64.60% | 67.50% | |
| DT | 62.35% | 61.35% | 61.18% | 61.40% | 60.89% | |
| RF | 64.77% | 72.54% | 64.77% | 61.50% | 60.04% | |
| Avg. | 64.77% | 67.39% | 64.39% | 62.99% | 63.52% | |
| Eye | MLP | 79.61% | 80.62% | 79.04% | 78.84% | 79.04% |
| SVM | 74.97% | 75.97% | 74.97% | 74.70% | 75.96% | |
| DT | 62.35% | 64.37% | 62.22% | 59.70% | 60.25% | |
| RF | 77.19% | 77.58% | 77.19% | 77.10% | 81.06% | |
| Avg. | 73.53% | 74.64% | 73.36% | 72.59% | 74.08% | |
| Fusion | MLP | 77.62% | 78.66% | 77.62% | 77.71% | 77.41% |
| SVM | 76.41% | 76.44% | 76.41% | 75.80% | 82.01% | |
| DT | 67.19% | 70.65% | 67.19% | 69.8% | 70.17% | |
| RF | 72.75% | 73.89% | 72.75% | 72.10% | 78.86% | |
| Avg. | 73.49% | 74.91% | 73.49% | 73.85% | 77.11% |