Abstract
Locally Linear Embedding (LLE) is a recently proposed algorithm for non-linear dimensionality reduction and manifold learning. However, it may not be optimal for classification problem. In this paper, an improved version of LLE, namely KFDA-LLE, is proposed using kernel Fisher discriminant analysis (KFDA) method, combined with SVM classifier for face recognition task. Firstly, the input training samples are projected into the low-dimensional space by LLE. Then KFDA is introduced for finding the optimal projection direction. Finally, SVM classifier is used for face recognition. Experimental results on face database demonstrate that the extended LLE method is more efficient and robust.
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Wang, G., Gao, C. (2011). Robust Face Recognition Based on KFDA-LLE and SVM Techniques. In: Lin, S., Huang, X. (eds) Advances in Computer Science, Environment, Ecoinformatics, and Education. CSEE 2011. Communications in Computer and Information Science, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23321-0_91
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DOI: https://doi.org/10.1007/978-3-642-23321-0_91
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