Name
Abstract | ||
|---|---|---|
Facial age estimation is an important problem in the field of computer image processing. Because of the difficulty of data collection, one of the most challenges of facial age estimation is that there are not sufficient training data. Label distribution learning is an effective method to address this problem, where its motivation is that facial aging information on adjacent ages can be introduced to enhance the age estimation model due to the fact that human faces change slowly on adjacent ages. Given a certain age to learn, label distribution learning converts the learning target from a continuous value to an age label distribution which is generated according to the description degree of the neighboring ages to the given age. Despite of the successful application of label distribution learning on facial age estimation, the existed methods have some obvious drawbacks. The method based on the maximum entropy builds separated model for each age and has a strong assumption about the data distribution, and the neural network based method has the problem of overfitting. In this paper, we transform the label distribution learning based age estimation problem into the multivariate multiple regression analysis and then solve it by the multivariate partial least squares regression. The proposed method has no assumption about the data distribution and builds an integrated effective model for all ages. Extensive comparative experimental results on the FG-NET dataset show that the proposed partial least squares regression based facial age estimation method has significantly better performance than the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CSE-EUC.2017.81 | 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) |
Keywords | Field | DocType |
facial age estimation,partial least squares regression,multivariate multiple regression,label distribution learning | Data collection,Computer science,Regression analysis,Multivariate statistics,Partial least squares regression,Image processing,Overfitting,Principle of maximum entropy,Statistics,Artificial neural network | Conference |
Volume | ISSN | ISBN |
1 | 1949-0828 | 978-1-5386-3222-2 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-qiang Zeng | 1 | 76 | 7.91 |
| Run Xiang | 2 | 1 | 0.68 |
| Hua-Xing Zou | 3 | 0 | 0.34 |