Sample pair based sparse representation classification for face recognition
作者：
Highlights：
• We proposed a method to use virtual available facial images for face recognition.
• We improved the linear regression classification method for face recognition.
• The classification accuracy on sample pairs are better than that on original samples.
• This method achieves lower classification error rates than many other methods.
• This method performs well even when there are few training samples of each class.
摘要
•We proposed a method to use virtual available facial images for face recognition.•We improved the linear regression classification method for face recognition.•The classification accuracy on sample pairs are better than that on original samples.•This method achieves lower classification error rates than many other methods.•This method performs well even when there are few training samples of each class.
论文关键词：Sparse representation classification,Pattern recognition,Face recognition
论文评审过程：Received 12 March 2015, Revised 15 July 2015, Accepted 30 September 2015, Available online 13 October 2015, Version of Record 10 November 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.09.058
