Incremental graph regulated nonnegative matrix factorization for face recognition

In a real world application,we seldom get all images at one time. considering this case,if a company hired an employee,all his images information needs to be recorded into the system; if we rerun the face recognition algorithm,it will be time consuming. to address this problem,in this paper,firstly,we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (ignmf) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (inmf); thus,our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly,considering we always get many face images belonging to one person or many different people as a batch,we improved our ignmf algorithms to batch-ignmf algorithms (b-ignmf),which implements incremental study in batches. experiments show that (1) the recognition rate of our ignmf and b-ignmf algorithms is close to gnmf algorithm while it runs faster than gnmf. (2) the running times of our ignmf and b-ignmf algorithms are close to inmf while the recognition rate outperforms inmf. (3) comparing with other popular nmf-based face recognition incremental algorithms,our ignmf and b-ignmf also outperform then both the recognition rate and the running time. © 2014 zhe-zhou yu et al.