A complete discriminative subspace for robust face recognition

Aimed at the problem that linear discriminative analysis algorithms for face recognition usually miss discriminative information when reducing dimensions, and the problem that it is difficult to make full use of discriminative information both in rank space and null space at the same time, this paper proposes a novel method to construct a new subspace called complete discriminative subspace and its discriminant matrix without losing any discriminative information contained in original space. The dimension of the new subspace is much lower and the constructing procedure is simple and costless. The experimental results demonstrate that this method has better performance and efficiency than standard discriminative analysis algorithms for face recognition.