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Great success in face recognition has been achieved in recent years; however, complex variations and low-resolution images remain a challenge for unconstrained face recognition. Face recognition in video or image sets, which is known as image-set-based face recognition (ISFR), is one feasible solution to address this problem. Regularized nearest points (RNP) is an effective hull-based ISFR method which uses linear space as the input. However, nonlinearity usually exists when the input data contain complex structures, such as illumination and pose variations. Hence, we propose to map the input data to a higher dimensional feature space by using kernel functions, and we develop the kernel extension of the efficient iterative solver to find the regularized nearest points between two sets in higher dimensional feature space. We also exploit this kernel efficient iterative solver to improve the kernel convex hull image-set-based collaborative representation and classification method. The proposed kernelized fast algorithm improves the face recognition ability of RNP and significantly accelerates the kernel version hull-based ISFR methods. Experiments are performed on three benchmark face recognition video data sets. The experimental results illustrate the effectiveness of our proposed methods.

Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets