A New Discriminative Sparse Representation Method for Robust Face Recognition via l Regularization

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 10; pp. 2233 - 2242
• Main Authors: Yong Xu, Zuofeng Zhong, Jian Yang, Jane You, Zhang, David
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l ₂ regularization; Algorithm design and analysis; Classification; Closed-form solutions; Computational efficiency; Computer applications; Computing time; Efficient computation; Face recognition; Facial recognition technology; Feasibility studies; Image classification; Linear programming; Matching pursuit algorithms; Methods; Pattern recognition; Regularization; Representations; Robustness; sparse representation; State of the art; Training
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2016.2580572

Sparse representation has shown an attractive performance in a number of applications. However, the available sparse representation methods still suffer from some problems, and it is necessary to design more efficient methods. Particularly, to design a computationally inexpensive, easily solvable, a...