Local maximal margin discriminant embedding for face recognition

• Class label information is considered in the procedure of feature extraction.
• Local structure is kept in the feature space.
• Locality preserving may produce high between-class overlaps by using the k nearest neighbor criterion.
• Maximizing the dissimilarity of samples in a manifold is beneficial for classification.

In this paper, a manifold learning based method named local maximal margin discriminant embedding (LMMDE) is developed for feature extraction. The proposed algorithm LMMDE and other manifold learning based approaches have a point in common that the locality is preserved. Moreover, LMMDE takes consideration of intra-class compactness and inter-class separability of samples lying in each manifold. More concretely, for each data point, it pulls its neighboring data points with the same class label towards it as near as possible, while simultaneously pushing its neighboring data points with different class labels away from it as far as possible under the constraint of locality preserving. Compared to most of the up-to-date manifold learning based methods, this trick makes contribution to pattern classification from two aspects. On the one hand, the local structure in each manifold is still kept in the embedding space; one the other hand, the discriminant information in each manifold can be explored. Experimental results on the ORL, Yale and FERET face databases show the effectiveness of the proposed method.