Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 54-60.doi: 10.6040/j.issn.1672-3961.0.2017.420

• Machine Learning & Data Mining • Previous Articles     Next Articles

Facial age estimation based on multivariate multiple regression

Run XIANG1(),Sufen CHEN2,Xueqiang ZENG3,*()   

  1. 1. Information Engineering School, Nanchang University, Nanchang 330031, Jiangxi, China
    2. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, Jiangxi, China
    3. School of Computer & Information Engineering, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Received:2017-08-24 Online:2019-04-20 Published:2019-04-19
  • Contact: Xueqiang ZENG E-mail:xiangr214@foxmail.com;xqzeng@jxnu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61463033);国家自然科学基金资助项目(61866017);江西省杰出青年人才资助计划资助项目(20171BCB23013);江西省自然科学基金资助项目(20151BAB207028)

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Abstract:

Label distribution learning based facial age estimation model was an effective method to solve the problem of insufficient training data caused by the difficulty of facial image collection, where its motivation was that facial aging information on adjacent ages can be introduced to enhance the age estimation model due to human faces changing slowly. Given a certain age to learn, label distribution learning converted the learning target from a continuous value to an age label distribution vector, which was generated according to the description degree of the neighboring ages. However, the existed methods had the drawbacks of separated age prediction model (maximum entropy based methods) or tending to be overfitting (neural network based methods). So a method of facial age estimation based on multivariate multiple regression was proposed, the label distribution learning based age estimation problem was transformed into a multivariate multiple regression analysis task and then solved by the multivariate partial least squares regression. Multivariate partial least squares regression had no assumption about the data distribution and built an integrated effective model for all ages even when there is a strong correlation among independent variables. Extensive comparative experimental results on FG-NET facial age estimation dataset showed that the proposed method significantly improved the training efficiency, and at the same time, had higher age estimation accuracy than the state-of-the-art methods.

Key words: facial age estimation, multivariate multiple regression, partial least squares regression, label distribution learning, least square regression

CLC Number: 

  • TP391

Fig.1

Part of facial images in the FG-NET database (the number under each picture corresponding to its chronological age)"

Fig.2

MAE of PLS-LLD and PLSR with the variance of the number of latent components"

Fig.3

MAE of PLS-LLD and CPNN-LLD with the variance of the standard deviation of age label Gaussian distribution"

Table 1

MAE and training time of four different age estimators"

方法 MAE 训练时间/s
PLS-LLD 4.55±3.29 1.65±0.54
PLSR 5.75±2.42 0.27±0.43
ⅡS-LLD 6.36±4.15 1481.32±16.87
CPNN-LLD 5.34±3.74 613.65±15.84
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