Table 3.
Comparative analysis of performance with different handcrafted features with conventional learning and deep learning approach for facial age prediction techniques: MAE, is measured for AGE Regression(R) while recognition error or accuracy is evaluated for age group Classification(C)
| Authors | Dataset | Feature Extraction | R/C | MAE Accuracy | |
|---|---|---|---|---|---|
| Conventional learning | Kwon et al. [56] | Private | Statistics Method | C | −−− |
| Ramesha et al. [51] | Private | Global,Grid | C | 90% | |
| Pirozmand [79] | FG-NET | Gabor-PCA+LDA | C | 90% | |
| Chikkala [17] | FGNET | WFPDP-GLCM | C | 96.5% | |
| MORPH | 97.5% | ||||
| Hong et al. [46] | MORPH-II | Bisection Search | R | 3.64 | |
| tree(BST) | |||||
| Guo et al. [40] | YGA | BIF,age manifold | R | 3.91 | |
| FG-NET | 4.77 | ||||
| Guo et al. [39] | YGA | BIF+MFA(Marginal | R | 2.63 | |
| Fisher Analysis) | |||||
| Guo et al. [37] | MORPH | Kernel Partial Least | R | 4.18 | |
| Squares (KPLS) | |||||
| Chang et al.[12] | MORPH-II | BIF Scattering | R | 3.74 | |
| Transform | |||||
| Hsu et al. [47] | MORPH | CBIF(Component | R | 3.21 | |
| FG-NET | Bioinspired feature) | 3.38 | |||
| Suo et al. [89] | FG-NET | AND-OR Graph | R | 4.68 | |
| Geng et al. [35] | FG-NET | AAM | R | 6.77 | |
| Chao et al. [14] | FG-NET | AAM | R | 4.4 | |
| Geng et al. [33] | FG-NET | IIS-LLD | R | 5.77 | |
| Fu et al. [31] | UIUC-IFP | Discriminative | R | 3.0 | |
| Aging Manifold | |||||
| Luu et al. [70] | FG-NET | Contourlet | R | 4.12 | |
| appearance | |||||
| Thukral et al. [91] | FG-NET | 2Dshape Grass- | R | 6.2 | |
| mann manifold | |||||
| Deep learning | Wang et al. [96] | FG-NET | CNN | R | 4.26 |
| MORPH-II | 4.77 | ||||
| Niu et al. [75] | MORPH-II | CNN | R | 3.42 | |
| Rothe et al. [81] | LAP | CNN | R | 5.007 | |
| Rothe et al. [83] | MORPH-II | DEX | R | 3.25 | |
| MORPH-II | DEX (fine | 2.68 | |||
| FG-NET | tune IMDB-WIKI) | 3.09 | |||
| Chen et al. [16] | MORPH | Ranking CNN | R | 2.96 | |
| Pan et al. [77] | FG-NET | CNN | R | 2.68 | |
| MORPH-II | 2.16 | ||||
| Zhang et al. [104] | FG-NET | AL-RoR-34 | R | 2.39 | |
| MORPH | 2.36 | ||||
| Liu et al. [66] | MORPH | LSDML-ResNet 101 | R | 3.08 | |
| Taheri et al. [90] | FG-NET | DAG-VGG16 | R | 3.08 | |
| MORPH | 2.81 | ||||
| FG-NET | DAG-GoogLeNet | R | 3.05 | ||
| MORPH | 2.87 | ||||
| Li et al. [62] | FG-NET | BridgeNet | R | 2.56 | |
| MORPH | 2.38 | ||||
| Agbo et al. [5] | FG-NET | Lightweight CNN | R | 3.05 | |
| MORPH | 2.31 | ||||
| Liu et al. [67] | FG-NET | MA-SFV2 | R | 3.81 | |
| MORPH | 2.38 | ||||
| Wang et al. [97] | FG-NET | CSC+STD | R | 4.01 | |
| MORPH | Pooling | 3.66 | |||
| Liao [64] | FG-NET | Deep SRC+HSVR | R | 4.65 | |
| MORPH | 3.64 |