Extended interval type-II and kernel based sparse representation method for face recognition
作者：
Highlights：
• Unseen information is extracted using extended interval type 2 membership function.
• It also works where other Gaussian based membership functions are not applicable.
• Computational efficient: using sparsity concept which makes FR systems fast.
• It deals with uncertainty originating from non-linear variations in the features.
• It gives better results as compared to other state-of-art methods.
摘要
•Unseen information is extracted using extended interval type 2 membership function.•It also works where other Gaussian based membership functions are not applicable.•Computational efficient: using sparsity concept which makes FR systems fast.•It deals with uncertainty originating from non-linear variations in the features.•It gives better results as compared to other state-of-art methods.
论文关键词：Fuzzy logics,Kernel methods,Sparse representation,Face recognition
论文评审过程：Received 26 September 2017, Revised 12 September 2018, Accepted 13 September 2018, Available online 13 September 2018, Version of Record 20 September 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.032
