Abstract
In the past few years, face attributes have attracted much attention. In this paper, for the first time we combine the discriminant subspace learning technique with the idea of pattern reconstruction to build a face attribute classification framework. For the attribute considered, the framework firstly learns an attribute subspace by using a discriminant subspace learning method, which also has the capability of pattern reconstruction. The framework then reconstructs the attribute state of input query image with the learned subspace, and classifies face attribute based on minimum reconstruction error. By repeatedly using the classification framework for different attributes, we can achieve multiple classification results output. According to the output, we select matching objects for each given query image based on generalized hamming distance to realize face recognition. The proposed attribute classification framework and face recognition approach are validated on the public AR and Weizmann face databases. Experimental results demonstrate their effectiveness as compared with several related methods.
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Acknowledgements
The work described in this paper was fully supported by National Natural Science Foundation of China under Project Nos. 61272273 and 61073113, the 333 Engineering of Jiangsu Province under Project No. BRA2011175, and the Postgraduate Scientific Research and Innovation Plan of Jiangsu Province Universities under Project No. CXLX13_465.
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Wu, F., Jing, XY. Discriminant-Reconstruction Based Multiple Attribute Classifiers for Face Recognition. Natl. Acad. Sci. Lett. 40, 177–182 (2017). https://doi.org/10.1007/s40009-017-0543-8
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DOI: https://doi.org/10.1007/s40009-017-0543-8