Robust Faces Manifold Modeling: Most Expressive vs. Most Sparse Criterion

Abstract

Robust face image modeling under uncontrolled conditions is crucial for the current face recognition systems in practice. One approach is to seek a compact representation of the given image set which encodes the intrinsic lower dimensional manifold of them. Among others, Local Linear Embedding (LLE) is one of the most popular method for that purpose. However, it suffers from the following problems when used for face modeling: 1) it is not robust under uncontrolled conditions (e.g., the underlying images may contain large appearance distortions such as partial occlusion or extreme illumination variations); 2) a fixed neighborhood size is used for all the local patches without considering the actual distribution of samples in the input space; 3) the modeled local structures may not contain enough discriminative information which is essential to the later recognition stage. In this paper, we introduce the Sparse Locally Linear Embedding (SLLE) to address these issues. By replacing the most-expressive type criterion in modeling local patches in LLE with a most-sparse one, SLLE essentially finds and models more discriminative patches. This gives higher model flexibility in the sense of less sensitiveness to incorrect model and higher robustness to outliers. The feasibility and effectiveness of the proposed method is verified with encouraging results on a publicly available face database.