Face Image Set Recognition Based on Bilinear Regression
Image sets-based face recognition receives growing research interest in pattern recognition and machine learning.The most challenging problem focuses on how to formulate a computable and discriminative model by using given data sets.In this paper,we propose a new method,which is called Bilinear Regression Classifier(BLRC)for short,to address the image sets-based face recognition problem.BLRC classifies a given test set by choosing the category that simultaneously maximizes the unrelated subspace and minimize the related subspace.In particular,the unrelated subspace is used to characterize the distances between the query set and the unrelated image sets,while the related subspace is used to characterize the distances between the query set and the related sets.In our work,the Mahalanobis metric,rather than the Euclidean metric,is exploited to compute the subspace distance.The subspace coefficient vectors are obtained by solving an Elastic-Net regularized regression model.Extensive experiments are conducted on several benchmark datasets to evaluate the real recognition performance of the new method.The results show that our BLRC method obtains competitive accuracies with some state-of-the-art methods.
Face recognition Image sets Linear regression
Wen-Wen Hua Chuan-Xian Ren
School of Mathematics,Sun Yat-sen University,Guangzhou 510275,China School of Mathematics,Sun Yat-sen University,Guangzhou 510275,China;Shenzhen Research Institute of S
国际会议
广州
英文
233-244
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）