An application study of manifold learning-ranking techniques in face recognition

Locally linear embedding (LLE), isometric mapping (Isomap) are two relatively new nonlinear dimensionality reduction algorithms also used in face recognition applications. Their main aim is to create a low-dimensional embeddings of the original high-dimensional data, laying the face data points on a 'face manifold'. In this work in order to test their performance we applied LLE and Isomap in two face databases together with principal component analysis (PCA), their linear counterpart, varying as parameters the (i) number embedding dimensions and (ii) the number of neighbours. Furthermore, at the final stage we used a data ranking algorithm, which ranks the data with respect to the intrinsic manifold structure and its geometric properties. Experimental results indicate the superiority of the data ranking algorithm on face manifolds against the classical Euclidean distance measure.