Metaface Learning for Sparse Representation based Face Recognition

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Authors Meng Yang, Lei Zhanga, Jian Yang and David Zhang
Journal/Conference Name 2010 IEEE International Conference on Image Processing (ICIP 2010)
Paper Category
Paper Abstract Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.
Date of publication 2010
Code Programming Language MATLAB

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