Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 35, Issue 10, Pages 2401-2412Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2013.51
Keywords
Age estimation; face image; label distribution; machine learning
Funding
- National Science Foundation of China [60905031, 61273300, 61073097, 61105043, 61232007]
- Jiangsu Science Foundation [BK2009269]
- National Fundamental Research Program of China [2010CB327903]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
- Excellent Young Teachers Program of SEU
- Open Projects Program of National Laboratory of Pattern Recognition
- Key Lab of Computer Network and Information Integration of Ministry of Education of China
- Australian Research Council [DP0987421]
- Australian Research Council [DP0987421] Funding Source: Australian Research Council
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One of the main difficulties in facial age estimation is that the learning algorithms cannot expect sufficient and complete training data. Fortunately, the faces at close ages look quite similar since aging is a slow and smooth process. Inspired by this observation, instead of considering each face image as an instance with one label (age), this paper regards each face image as an instance associated with a label distribution. The label distribution covers a certain number of class labels, representing the degree that each label describes the instance. Through this way, one face image can contribute to not only the learning of its chronological age, but also the learning of its adjacent ages. Two algorithms, named IIS-LLD and CPNN, are proposed to learn from such label distributions. Experimental results on two aging face databases show remarkable advantages of the proposed label distribution learning algorithms over the compared single-label learning algorithms, either specially designed for age estimation or for general purpose.
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