A robust face super-resolution algorithm and its application in low-resolution face recognition system

In real-world surveillance scenario, the face recognition (FR) systems pose a lot of challenges due to the captured low-resolution (LR) and noisy probe images. A new face super-resolution (SR) algorithm is proposed to design a recognition model overcoming the challenges of existing FR systems. The p...

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Bibliographic Details
Published in:Multimedia tools and applications 2020-09, Vol.79 (33-34), p.23909-23934
Main Authors: Rajput, Shyam Singh, Arya, K. V.
Format: Article
Language:English
Subjects:
Algorithms
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Dictionaries
Face recognition
Facial recognition technology
Image reconstruction
Image resolution
Interpolation
Multimedia Information Systems
Object recognition
Special Purpose and Application-Based Systems
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Summary:In real-world surveillance scenario, the face recognition (FR) systems pose a lot of challenges due to the captured low-resolution (LR) and noisy probe images. A new face super-resolution (SR) algorithm is proposed to design a recognition model overcoming the challenges of existing FR systems. The proposed SR algorithm inherits the merits of functional-interpolation and dictionary-based SR techniques. The functional interpolation assists in generating more discriminable output, whereas the dictionary-based approach assists in eliminating noise effects from the reconstruction process. Consequently, it produces more discriminable and noise-free high-resolution (HR) images from captured noisy LR probe images, suitable for real-world problems like low-resolution face recognition. The results obtained from the experiments performed on several popular face image datasets including FEI, FERET, and CAS-PEAL-R1 show that the proposed algorithm performs better than all the comparative SR methods.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09072-5