Abstract
A novel subspace method is proposed for part-based face recognition by using non-negative matrix factorization with sparseness constraints (NMFs) and Fisher’s linear discriminant (FLD) hence its abbreviation, FNMFs. A comparative analysis engages PCA+FLD (FPCA) method and FNMFs method for both part-based and holistic-based face recognition. The comparative experiments are completed for the ORL face database and UMIST face database, it shows that FNMFs has better performance than FPCA-based method both for holistic-face and parts-face images recognition.
This work was supported by National Science Foundation of China under Grant 60471055, Specialized Research Fund for the Doctoral Program of Higher Education under Grant 20040614017, and the UESTC Youth Fund under Grant L08010601JX04030.
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The ORL database is available from http://www.cam-orl.co.uk/facedatabase.html
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Pu, X., Yi, Z., Zheng, Z., Zhou, W., Ye, M. (2005). Face Recognition Using Fisher Non-negative Matrix Factorization with Sparseness Constraints. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_19
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DOI: https://doi.org/10.1007/11427445_19
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