Single sample face recognition based on virtual images and 2DLDA

Jun Yang; Yanli Liu

doi:10.1117/12.2064163

16 April 2014 Single sample face recognition based on virtual images and 2DLDA

Jun Yang, Yanli Liu

Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 91590L (2014) https://doi.org/10.1117/12.2064163
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece

Abstract

When there is only one sample per person in gallery set, the conventional face recognition methods which work with many training samples do not work well. Especially, a number of methods based on Fisher linear discrimination criterion cannot work because the within-class scatter matrix is a matrix with all elements being zero. To solve this problem, a method was proposed to get virtual sub images of one face by an image processing method. With these virtual images, the within-class scatter matrix can be evaluated and the supervised learning method such as 2D fisher linear discrimination analysis can be utilized for feature extraction. The experimental results on ORL face database show that the proposed method is efficient and it can achieve higher recognition accuracy than others.

Citation Download Citation

Jun Yang and Yanli Liu "Single sample face recognition based on virtual images and 2DLDA", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91590L (16 April 2014); https://doi.org/10.1117/12.2064163

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