Fractional Multiset Coherent Super-Resolution Representation for Low Resolution Face Recognition
- Resource Type
- Conference
- Authors
- Yuan, Yun-Hao; Li, Jin; Li, Yun; Qiang, Jipeng; Zhu, Yi; Yang, Yuequan; Shen, Xiaobo
- Source
- 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Cloud Computing and Intelligent Systems (CCIS), 2022 IEEE 8th International Conference on. :155-159 Nov, 2022
- Subject
- Computing and Processing
Training
Cloud computing
Databases
Face recognition
Superresolution
Benchmark testing
Random variables
Multiset partial least squares
Super resolution
Low resolution
- Language
In this paper, we address the problem of multiple resolution simultaneous learning in the limited training samples or noise disturbance cases and propose a novel fractional multiset partial least squares (FMPLS) approach for simultaneously dealing with multiset high dimensional data. The proposed FMPLS reconstructs the sample covariance matrices by fractional order spectral decomposition. Through using this FMPLS as a tool, we further present a new fractional multiset coherent super-resolution representation (FMCSR) method for low-resolution face recognition. Experimental results on two benchmark face databases demonstrate the effectiveness of the proposed FMCSR method.