Collaboratively Regularized Nearest Points for Set Based Recognition

Set based recognition has been attracting more and more attention in recent years, benefitting from two facts: the difficulty of collecting sets of images for recognition fades quickly, and set based recognition models generally outperform the ones for single instance based recognition. In this paper, we propose a novel model called collaboratively regularized nearest points (CRNP) for solving this problem. The proposal inherits the merits of simplicity, robustness, and high-efficiency from the very recently introduced regularized nearest points (RNP) method on finding the set-to-set distance using the l2-norm regularized affine hulls. Meanwhile, CRNP makes use of the powerful discriminative ability induced by collaborative representation, following the same idea as that in sparse recognition for classification (SRC) for image-based recognition and collaborative sparse approximation (CSA) for set-based recognition. However, CRNP uses l2-norm instead of the expensive l1-norm for coefficients regularization, which makes it much more efficient. Extensive experiments on five benchmark datasets for face recognition and person re-identification demonstrate that CRNP is not only more effective but also significantly faster than other state-of-the-art methods, including RNP and CSA.