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文件名称：Iterative Re-constrained Group Sparse Face Recognition
文件大小：4.24MB
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更新时间：2021-12-25 07:42:52
论文
In this paper, we consider the robust face recognition
problem via iterative re-constrained group sparse classifier with
adaptive weights learning (IRGSC). Specifically, we propose a
group sparse representation classification (GSRC) approach in
which weighted features and groups are collaboratively adopted
to encode more structure information and discriminative information than other regression based methods. In addition, we
derive an efficient algorithm to optimize the proposed objective
function, and theoretically prove the convergence. There are several appealing aspects associated with IRGSC. First, adaptively
learned weights can be seamlessly incorporated into the GSRC
framework. This integrates the locality structure of the data and
validity information of the features into l2;p-norm regularization
to form a unified formulation. Second, IRGSC is very flexible to
different size of training set as well as feature dimension thanks to
the l2;p-norm regularization. Third, the derived solution is proved
to be a stationary point (globally optimal if p≥1). Comprehensive
experiments on representative datasets demonstrate that IRGSC
is a robust discriminative classifier which significantly improves
the performance and efficiency compared with the state-of-theart methods in dealing with face occlusion, corruption, and
illumination changes, etc.