Facial age range estimation with extreme learning machines

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Facial age range estimation with extreme learning machines
Facial age range estimation with extreme learning machines
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07 September 2014
Phyo-Kyaw Sai, Jian-Gang Wang, Eam-Khwang Teoh, Facial age range estimation with extreme learning machines, Neurocomputing, Volume 149, Part A, 3 February 2015, Pages 364-372, ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2014.03.074.
Face image based age estimation is an approach to classify face images into one of several pre-defined age-groups. It is challenging because facial aging variation is specific to a given individual and is determined by the person's gene and many external factors, such as exposure, weather, gender, and living style. Age estimation is a multiclass problem and the number of classes to predict is quite large. There surely is facial aging trend and faces from closed age range have some similar facial aging features. It is difficult to say there are distinct facial aging features for an age. Facial aging features are found to be overlapped among nearby age groups along the aging life and are continuous in nature. In this paper, we emphasised our work on age range estimation with four pre-defined classes. We applied a fast and efficient machine learning method: extreme learning machines, to solve the age categorization problem. Local Gabor Binary Patterns, Biologically Inspired Feature and Gabor were adopted to represent face image. Age estimation was performed on three different aging datasets and experimental results are reported to demonstrate its effectiveness and robustness.
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