Feature-organized sparseness for efficient face recognition from multiple poses

Abstract

Automatic and real-time face recognition can be applied into many attractive applications. For example, at a checkpoint it is expected that there are no burdens on a passing person and a security guard in addition to low cost. Normally a unique 3D person is projected into 2D images with information loss. It means a person is no longer unique in 2D space. Furthermore the various conditions such as pose variance, illumination variance and different expression make face recognition difficult. In order to separate a person, his or her subspace should have several faces and be redundant. That is why the database naturally becomes large. Under this situation the efficient face recognition is a key to a surveillance system. Face recognition by spars representation classification (SRC) could be one of promising candidates to realize rapid face recognition. This method can be understood in a similar way to compressive sensing (CS). In this paper, we propose the efficient approach of face recognition by SRC for multiple poses from the viewpoint of CS. The part-cropped database (PCD) is suggested to avoid position misalignments by discarding the information of topological linkages among eyes, a nose and a mouth. Although topological linkages are important for face recognition in general, they cause position misalignments among multiple poses which decrease recognition rate. Our approach solves one of trade-off problem between keeping topological linkages and avoiding position misalignments. According to the simulated experiments, PCD works well to avoid position misalignments and acquires correct recognition despite less information on topological linkages.