Title :
Eye Detection Based on Principal Component Template and Nonlinear Correlation
Author :
Ruiming Liu ; Qi Jiang
Author_Institution :
Sch. of Electron. Eng., Huaihai Inst. of Technol., Lianyungang, China
Abstract :
A novel template matching method is proposed for eye detection. the classic template matching methods directly use images as templates. They are susceptible to variations in scale and light conditions. Moreover, the linear correlation coefficient is used to measure the matching degree without considering the higher-order statistics of images. Unlike the classic template matching, the projection coefficients of principal components are used as templates and the non-linear correlation coefficients for capturing the higher-statistic features is proposed to measure the matching degree. Moreover, the reduction of computation costs is achieved by taking advantage of the eye symmetry.
Keywords :
correlation theory; eye; higher order statistics; image matching; object detection; principal component analysis; eye detection; eye symmetry; higher order statistics; light condition variation; linear correlation coefficient; nonlinear correlation coefficient; principal component analysis; projection coefficient; scale condition variation; template matching method; Correlation; Face; Higher order statistics; Kernel; Principal component analysis; Vectors; Eye detection; Nonlinear correlation; PCA; Projection coefficient; Template matching;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2646-9
DOI :
10.1109/ISCID.2012.16