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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)