Multifeature Extreme Ordinal Ranking Machine for Facial Age EstimationReport as inadecuate

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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 840840, 9 pages -

Research ArticleSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798

Received 12 May 2015; Accepted 23 August 2015

Academic Editor: Huaguang Zhang

Copyright © 2015 Wei Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine MFEORM for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime even up to nearly one hundred times faster while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.

Author: Wei Zhao, Han Wang, and Guang-Bin Huang



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