Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression

• Published in: IEEE transactions on image processing Vol. 17; no. 7; pp. 1178 - 1188
• Main Authors: Guodong Guo, Yun Fu, Dyer, C.R., Huang, T.S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adjustment; Age; Age manifold; Aging; Aging - physiology; Algorithms; Application software; Applied sciences; Artificial Intelligence; Computer science; control theory; systems; Computer vision; Estimating; Exact sciences and technology; Face; Face - anatomy & histology; Face - physiology; Feature extraction; Human; human age estimation; Human computer interaction; Humans; Image analysis; Image databases; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Information, signal and communications theory; Learning; locally adjusted robust regression; manifold learning; Manifolds; Multimedia communication; Multimedia communications; nonlinear regression; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Regression Analysis; Reproducibility of Results; Robustness; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Studies; support vector machine (SVM); support vector regression (SVR); Telecommunications and information theory; Vision systems; Weathering; Performance evaluation; nonlinear regression; Automatic classification; Age estimation; human age estimation; support vector machine (SVM); Vision system; Support vector machine; Multimedia communication; manifold learning; Learning; Accuracy; Database; Man machine relation; Localization; Age manifold; Computer vision; Meteorological phenomenon; Non linear regression; Ageing; Regression analysis; Vector method; Signal classification; Image analysis; locally adjusted robust regression; support vector regression (SVR)
• DOI: 10.1109/TIP.2008.924280
• ISSN: 1057-7149

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.