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Table 2 Comparison with state-of-the-art methods on FG-NET and MORPH databases

From: A novel comparative deep learning framework for facial age estimation

Method

Database (FG-NET)

Database (MORPH)

CRCNN (early fusion) (RCNN)

4.13

3.74±0.29

CRCNN (early fusion) (CNN)

4.72

4.33±0.27

CRCNN (late fusion) (RCNN)

4.20

3.81±0.32

CRCNN (late fusion) (CNN)

4.81

4.52±0.23

Ranking SVM [25]

5.24

6.49±0.17

RankBoost [26]

5.67

6.83±0.25

RankNet [27]

5.46

6.71±0.24

rKCCA [21]

-

3.98

rKCCA + SVM [21]

-

3.92

IIS-LLD [10] (Gaussian)

5.77

5.67±0.15

IIS-LLD [10] (Triangle)

5.90

6.09±0.14

IIS-LLD [10] (Single)

6.27

6.35±0.17

CPNN [10] (Gaussian)

4.76

4.87±0.31

CPNN [10] (Triangle)

5.07

4.91±0.29

CPNN [10] (Single)

5.31

6.59±0.31

OHRank [31]

6.27

6.28±0.18

AGES [37]

6.77

6.61±0.11

WAS [38]

8.06

9.21±0.16

AAS [39]

14.83

10.10±0.26

kNN [40]

8.24

9.64±0.24

BP [41]

11.85

12.59±1.38

C4.5 [42]

9.34

7.48±0.12

SVM [43]

7.25

7.34±0.17

ANFIS [44]

8.86

9.24±0.17

Human Tests (HumanA)

8.13

8.24

Human Tests (HumanB)

6.23

7.23

  1. The data in boldface means the best results of FG-NET and MORPH database are both from our CRCNN approach (with the early fusion scheme)