Illumination normalization for robust face recognition against varying lighting conditions
By Shiguang Shan, Wen Gao, Bo Cao, Debin Zhao. 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443), 2003
417 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/4a18adc7f5a090a041528a88166671248703f6e0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Conrad Sanderson
- Shiguang Shan
- Xilin Chen
- Wen Gao
- Luc Van Gool
- Yongkang Wong
- Shaokang Chen
- Sandra Mau
In Signal Processing
- Conrad Sanderson
- Brian C. Lovell
- Shiguang Shan
- Debin Zhao
- Meng Joo Er
- Shiqian Wu
- Weilong Chen
- Wen Gao
Citations in the corpus
- The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations (2008, methodology): “In the illumination normalization step, four illumination normalization methods are evaluated: histogram equalization (HE), gamma intensity correction (GIC), region-based HE (RHE), and region-based GIC (RGIC) [34].”
- Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition (2011, background): “While recent face recognition algorithms can handle faces with moderately challenging illumination conditions [15, 17, 24, 28], strong illumination variations (causing cast shadows and self-shadowing) remain problematic [30].”
- Face Recognition Under Varying Illumination Using Gradientfaces (2009, background): “For instance, histogram equalization (HE) [5], [6], logarithm transform [7] are widely used for illumination normalization.”
- A Natural Visible and Infrared Facial Expression Database for Expression Recognition and Emotion Inference (2010, methodology): “Furthermore, four methods are used to normalize the grey-level values of the images, that is, histogram equalization (HE), regional histogram equalization (RHE), gamma transformation (GT), and regional gamma transformation (RGT) [25].”
- Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, neutral): “Output Raw images”
- Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain (2006, methodology): “In [3], the authors proposed a normalization method called quotient illumination relighting (QIR).”