Cross-Age Face Recognition Based on Deep Learning

Table 1. Recognition rate of Rank-1 for different values of $λ_{1}$ and $λ_{2}$
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Table 1. Recognition rate of Rank-1 for different values of $λ_{1}$ and $λ_{2}$
$λ_{1}$ $λ_{2}$ Accuracy of Rank-1 /%
0.01 1 99.03
0.005 1 99.00
0.015 1 98.80
0.01 0.5 98.56
0.02 1 98.67

Table 2. Rank-1 results of different algorithms on Morph Album 2

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Table 2. Rank-1 results of different algorithms on Morph Album 2

Algorithm	Accuracy of Rank-1 /%
HFA（2013）^［10］	91.14
MEFA（2015）^［11］	93.80
LF-CNN（2016）^［12］	97.51
AE-CNN（2017）^［14］	98.13
AFJT-CNN（2018）^［34］	97.85
JMCNN（2018）^［26］	98.36
OE-CNN（2018）^［15］	98.67
DAL（2019）^［16］	98.97
FSDS-CNN（2020）^［32］	98.41
Baseline	95.67
Baseline+Age	97.73
Proposed algorithm	99.03

Table 3. Evaluation results of different algorithms on CALFW dataset

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Table 3. Evaluation results of different algorithms on CALFW dataset

Algorithm	Training image size /Million	EER /%	$F N M R @ F M R (0.1)$ /%
VGG-Face（2015）^［35］	2.6	13.5	17.6
Nosiy softmax（2017）^［36］	0.5	17.5	29.2
AFJT-CNN（2018）^［34］	0.7	14.8	21.8
FSDS-CNN（2020）^［32］	0.5	10.1	10.2
Proposed algorithm	0.5	9.8	10.0

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Wenbin Sun, Rong Wang, Lianzhu Sun, Yuansong Lin. Cross-Age Face Recognition Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0215001

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