Re-lighting and Compensation for Face Images

The illumination variance makes the robust face detection a challenging problem. Although there are some methods focusing on the de-lighting problem, the requirement for testing set or the 3D information limits the application for the detection tasks. According to the reflection function, the illumination takes the role of amplification for the reflective character of the surface. The shadows will make the features of the object attenuated or diminished. We introduce the radiance map ratio to adjust the image to a new illumination condition and apply the original image to compensate the adjusted image. The re-lighting and compensation makes the illumination condition uniform and keeps the original smooth changed information. As a pre-filter in the detector, the re-lighting and compensation method facilitates the performance of the detector.

[1]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[2]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[3]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Pat Hanrahan,et al.  A signal-processing framework for inverse rendering , 2001, SIGGRAPH.

[5]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  E. Land.  The retinex theory of color vision. , 1977, Scientific American.

[7]  A. Martínez,et al.  The AR face databasae , 1998 .

[8]  David J. Kriegman,et al.  What is the set of images of an object under all possible lighting conditions? , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[10]  P. Hanrahan,et al.  On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  David J. Kriegman,et al.  Illumination cones for recognition under variable lighting: faces , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[12]  Ralph Gross,et al.  An Image Preprocessing Algorithm for Illumination Invariant Face Recognition , 2003, AVBPA.

[13]  Aleix M. Martinez,et al.  The AR face database , 1998 .