Self quotient image for face recognition

• Published in: 2004 International Conference on Image Processing Vol. 2; pp. 1397 - 1400 Vol.2
• Main Authors: Haitao Wang, Li, S.Z., Yangsheng Wang, Jianjun Zhang
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Artificial intelligence; Automation; Computer displays; Computer science; control theory; systems; Dynamic range; Exact sciences and technology; Face recognition; Jacobian matrices; Lighting; Pattern recognition. Digital image processing. Computational geometry; Power harmonic filters; Printers; Reflectivity; Testing; Invariant; Image recognition; Filtering; Face recognition; Image processing; Shadow; Pattern recognition; Modeling; Fast filter; Bootstrapping; Alignment; Luminance; Lighting; Facies; Illumination; Tridimensional image; Reliability
• ISBN: 0780385543; 9780780385542
• ISSN: 1522-4880
• DOI: 10.1109/ICIP.2004.1419763

The reliability of facial recognition techniques is often affected by the variation of illumination, such as shadows and illumination direction changes. In this paper, we present a novel framework, called the self-quotient image, for the elimination of the lighting effect in the image. Although this method has a similar invariant form to the quotient image by Shashua etc. (2001), it does not need the alignment and bootstrap images. Our method combines the image processing technique of edge-preserved filtering with the Retinex applications of by Jobson, et al., (1997) and Gross and Brajovie (2003). We have analyzed this algorithm with a 3D imaging model and formulated the conditions where illumination-invariant and -variant properties can be realized, respectively. A fast anisotropic filter is also presented. The experiment results show that our method is effective in removing the effect of illumination for robust face recognition.