%A Run XIANG,Sufen CHEN,Xueqiang ZENG %T Facial age estimation based on multivariate multiple regression %0 Journal Article %D 2019 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2017.420 %P 54-60 %V 49 %N 2 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1809.shtml} %8 2019-04-20 %X

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.