Face recognition using Elasticfaces

Though principle component analysis (PCA) and locality preserving projections (LPPs) are two of the most popular linear methods for face recognition, PCA can only see the Euclidean structure of the training set and LPP preserves the nonlinear submanifold structure hidden in the training set. In this paper, we propose the elastic preserving projections (EPPs) which by incorporating the merits of the local geometry and the global information of the training set. EPP outputs a sample subspace which simultaneously preserves the local geometrical structure and exploits the global information of the training set. Different from some other linear dimensionality reduction methods, EPP can be deemed as learning both the coordinates and the affinities between sample points. Furthermore, the effectiveness of our proposed algorithm is analyzed theoretically and confirmed by some experiments on several well-known face databases. The obtained results indicate that EPP significantly outperforms its other rival algorithms.

► EPP can discover the nonlinear submanifold structure hidden in face space.
► EPP sees the Euclidean structure and does not destroy total distribution of face space.
► EPP can be seen as learning both the coordinates and affinities between face images.