Fast neighbourhood component analysis with spatially smooth regulariser for robust noisy face recognition
- Author(s): Faqiang Wang 1 ; Hongzhi Zhang 1 ; Kuanquan Wang 1 ; Wangmeng Zuo 1
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View affiliations
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Affiliations:
1:
Computational Perception and Cognition Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, People's Republic of China
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Affiliations:
1:
Computational Perception and Cognition Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, People's Republic of China
- Source:
Volume 3, Issue 4,
December 2014,
p.
278 – 290
DOI: 10.1049/iet-bmt.2013.0087 , Print ISSN 2047-4938, Online ISSN 2047-4946
For the robust recognition of noisy face images, in this study, the authors improved the fast neighbourhood component analysis (FNCA) model by introducing a novel spatially smooth regulariser (SSR), resulting in the FNCA-SSR model. The SSR can enforce local spatial smoothness by penalising large differences between adjacent pixels, and makes FNCA-SSR model robust against noise in face image. Moreover, the gradient of SSR can be efficiently computed in image space, and thus the optimisation problem of FNCA-SSR can be conveniently solved by using the gradient descent algorithm. Experimental results on several face data sets show that, for the recognition of noisy face images, FNCA-SSR is robust against Gaussian noise and salt and pepper noise, and can achieve much higher recognition accuracy than FNCA and other competing methods.
Inspec keywords: face recognition; gradient methods; statistical analysis
Other keywords: adjacent pixels; robust noisy face recognition; gradient descent algorithm; local spatial smoothness; face data sets; spatially smooth regulariser; optimisation problem; Gaussian noise; FNCA-SSR model; salt and pepper noise; fast neighbourhood component analysis
Subjects: Optimisation techniques; Optimisation techniques; Computer vision and image processing techniques; Other topics in statistics; Image recognition; Other topics in statistics
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