Table 14.
Comparative performance in terms of accuracy with the existing approaches–FEI dataset
| References | Classifier | Extracted features | Accuracy |
|---|---|---|---|
| Micheal and Geetha (2019) | SVM | DRLBP++RILPQ+PHOG | 95.30% |
| Geetha et al. (2019) | SVM | 8-LDP+LBP | 99% |
| Ghojogh et al. (2018) | LDA+weighting vote | Intensity of lower part of face | 94% |
| Haider et al. (2019) | Deepgender | * | 98.75% |
| Zhou and Li (2019) | GA-BPNN | Eigen-features based on PCA | 96% |
| Khan et al. (2019) | MSFS-CRFs | Segmentation based on Super-Pixels | 93.70% |
| Kumar et al. (2019) | SVM | Multi-features (BoW+SIFT) | 98% |
| Proposed method | AOA-BPNN | Multi-blocks HOG | 99.16% |
| Multi-blocks LBP | 95.61% | ||
| Multi-blocks GLCM | 99.04% |