Joint regularized nearest points for image set based face recognition

Face recognition based on image set has attracted much attention due to its promising performance to overcome various variations. Recently, (collaborative) regularized nearest points (C)RNP has achieved the state-of-art performance by measuring the between-set distance as the distance between nearest points generated in each image set. However, the nearest point of the query set in RNP changes in computing its distance to nearest points of different gallery image sets, which may result in that a wrong gallery image set can also has a small between-set distance; CRNP used collaborative representation to overcome this issue but it doesn't explicitly minimize the between-set distance. In order to solve these issues and fully exploit the advantages of nearest point based approaches, in this paper a novel joint regularized nearest points (JRNP) is proposed for face recognition based on image sets. In JRNP, the nearest point in the query set keeps the same when computing its distance to the image sets of different classes; at the same time, it explicitly minimize the between-set distance of facial images. An efficient algorithm was proposed to solve this problem, and the classification is then based on the joint distance between the regularized nearest points in image sets. Extensive experiments on benchmark databases were conducted on benchmark databases (e.g., Honda/UCSD, CMU Mobo, and YouTube databases). The experimental results clearly show that our JRNP leads the performance in face recognition based on image sets.