Abstract
It is very meaningful for dimension reduction by extraction and analysis of the underlying manifold embedded in face observation space, since the low dimensional manifold can represent the varying intrinsic features. However, this kind of manifold is perhaps not useful for face image recognition problem. This paper proposes a new discriminative manifold learning method which can efficiently discover the discriminative manifold. Besides the characteristic of preserving the local structure similarity in the face submanifold, the proposed method emphasizes the discriminative property of embedding much more throughout building and solving an object function. Experimental results on some open face datasets indicate the proposed method can achieve lower error rates.
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Niu, Y., Wang, X. (2006). Extraction of Discriminative Manifold for Face Recognition. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_22
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DOI: https://doi.org/10.1007/11893257_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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