A paired sparse representation model for robust face recognition from a single sample
作者：
Highlights：
• A paired sparse representation model for still-to-video face recognition, using generic learning and data augmentation to represent both linear and non-linear variations based on a single reference still ROI.
• A simultaneous optimization technique is proposed to encourage the augmented gallery dictionary to share the same sparsity pattern with a similar pose angle in the auxiliary dictionary.
• A method to design a compact augmented dictionary using row sparsity for SRC which reduce the time and memory complexity.
摘要
•A paired sparse representation model for still-to-video face recognition, using generic learning and data augmentation to represent both linear and non-linear variations based on a single reference still ROI.•A simultaneous optimization technique is proposed to encourage the augmented gallery dictionary to share the same sparsity pattern with a similar pose angle in the auxiliary dictionary.•A method to design a compact augmented dictionary using row sparsity for SRC which reduce the time and memory complexity.
论文关键词：Face recognition,Sparse representation-based classification,Face synthesis,Generic learning,Simultaneous sparsity,Video surveillance
论文评审过程：Received 11 February 2019, Revised 4 October 2019, Accepted 24 November 2019, Available online 25 November 2019, Version of Record 28 November 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107129
