Face recognition using extended isomap

The Isomap method has demonstrated promising results in finding low dimensional manifolds from samples in the high dimensional input space. While conventional subspace methods compute L₁ or L₂ metrics to represent distances between samples and apply Principal Component Analysis or alike to induce linear manifolds, the Isomap method estimates geodesic distance between samples and then uses multidimensional scaling to induce a low dimensional manifold. Since the Isomap method is developed based on reconstruction principle, it may not be optimal from the classification viewpoint. In this paper, we present an extended Isomap method that utilizes-Fisher Linear Discriminant for pattern classification. Numerous experiments on image data sets show that our extension is more effective than the original Isomap method for pattern classification. Furthermore, the extended Isomap method shows promising results compared with best classification methods in the literature.