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Proceedings Paper

Age estimation using active appearance model combining with local texture features
Author(s): Chunhua Xie; Zhenming Peng; Lingbing Peng; Yang Yong
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Paper Abstract

In this paper, a novel age estimation method by using active appearance model (AAM) combining with local texture feature is presented, which overcomes the drawbacks of the AAM. Use the multi-scale local binary patterns (MLBP) as the local texture descriptors to get the rotation invariant texture features. Build the combined AAM model using MLBP features. In this way, both global face features and local texture features are used. The support vector regression (SVR) is used to estimate the facial age. The face aging data set FG-NET is used. Experimental results demonstrate the AAM combined MLBP method performing a lower mean-absolute error (MAE) and high accuracy of estimation comparing to other method results.

Paper Details

Date Published: 30 November 2012
PDF: 10 pages
Proc. SPIE 8558, Optoelectronic Imaging and Multimedia Technology II, 85581E (30 November 2012); doi: 10.1117/12.981748
Show Author Affiliations
Chunhua Xie, Univ. of Electronic Science and Technology of China (China)
Zhenming Peng, Univ. of Electronic Science and Technology of China (China)
Lingbing Peng, Univ. of Electronic Science and Technology of China (China)
Yang Yong, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 8558:
Optoelectronic Imaging and Multimedia Technology II
Tsutomu Shimura; Guangyu Xu; Linmi Tao; Jesse Zheng, Editor(s)

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