Abstract
Subspace learning based face recognition methods have attracted considerable interests in recently years. However, the accuracies of previous methods are not so high, because they don’t utilize the manifold of face image data sufficiently and neglect some particular characters of the images. Thus a new method to form graph of data is proposed in this paper, and the method is used to develop two face recognition algorithms. At the same time the pixels correlation in images is considered sufficient under the constrain of spatially smooth in the two developed algorithms. So the features of the projected subspace based on our algorithms have better classification ability. Therefore, the right recognition rates are enhanced by the two proposed algorithms. This is further confirmed by experiments.
This work is partially supported by the National Foundation of China No. 70373046, and Ministry of Education, Humanities and Social Sciences Planning Fund No.09YJA630127.
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Wen, H., Wen, Y. (2011). Face Recognition Using Spatially Smooth and Maximum Minimum Value of Manifold Preserving. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_24
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DOI: https://doi.org/10.1007/978-3-642-23220-6_24
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