Kernelized dual regression incorporating local information for image set classification

摘要

In image set classification, dual linear regression classification (DLRC) has shown the excellent performance on face image data without the interference of the complex background. However, DLRC could not well identify the data set with the complex background. The complex background means that the background is cluttered and the viewpoint is unusual or the object is partially occluded. This paper pro poses a new model, kernelized dual regression (KDR), based on DLRC and the kernel trick which is a useful technique in image classification. Different from DLRC, KDR adopts a block partitioning strategy to extract the local information, which is able to conquer the shortcoming of DLRC. To capture the non linear relationship between the training set and test set, KDR tactfully maps these image sets into a high-dimensional feature space by adopting the nonlinear mapping associated with the Gaussian kernel function. In the reproducing kernel Hilbert space (RKHS), KDR can find the joint coefficients by minimizing the distance between training set and test set, and has a closed-form solution. Extensive experiments on four datasets show that KDR could achieve better classification performance than that of DLRC and other existing methods. (c) 2020 Elsevier B.V. All rights reserved.