Image Set-Oriented Dual Linear Discriminant Regression Classification and Its Kernel Extension

Along with the rapid development of computer and image processing technology, it is definitely convenient to obtain various images for subjects, which can be more robust to classification as more feature information is contained. However, how to effectively exploit the rich discriminative information within image sets is the key problem. In this paper, based on the concept of dual linear regression classification method for image set classification, we propose a novel discriminative framework to exploit the superiority of discriminant regression mechanism. We aim to learn a projection matrix to force the represented image points from the same class to be close and those from different class are better separated. The feature extraction strategy in our discriminative framework can appropriately work with the corresponding classification strategy, thus, better classification performance can be achieved. Moreover, we propose a kernel discriminative extension method to address the non-linearity problem by adopting the kernel trick. From the experimental results, our proposed method can obtain competitive recognition rates on face recognition tasks via mapping the original image sets into a more discriminative feature space. Besides, it also shows the effectiveness for object classification task with small image sizes and different number of frames.