Face Tracking Using the Dynamic Grey World Algorithm
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Tracking regions of human skin through illumination changes
Pattern Recognition Letters - Special issue: Colour image processing and analysis
A computer vision based human-robot interface
Autonomous robotic systems
A novel method for detecting lips, eyes and faces in real time
Real-Time Imaging - Special issue on spectral imaging
Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
American sign language recognition in game development for deaf children
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
An adaptive multiple model approach for fast content-based skin detection in on-line videos
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
One-click white balance using human skin reflectance
Proceedings of Graphics Interface 2009
Color based skin classification
Pattern Recognition Letters
Mixture models with skin and shadow probabilities for fingertip input applications
Journal of Visual Communication and Image Representation
Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
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Color is an important and useful feature for object tracking and recognition in computer vision. However, it has the difficulty that the color of the object changes if the illuminants color changes. But under known illuminant color it becomes a robust feature. There are more and more computer vision applications tracking humans, for example in interfaces for human computer interaction or automatic cameramen, where skin color is an often-used feature. Hence, it would be of significant importance to know the illuminant color in such applications.This paper proposes a novel method to estimate the current illuminant color from skin color observations. The method is based on a physical model of reflections, the assumption that illuminant colors are located close to the Planckian locus, and the knowledge about the camera parameters. The method is empirically tested using real images. The average estimation error of the correlated color temperature is as small as 180K. Applications are for example in color based tracking to adapt to changes in lighting and in visualization to re-render image colors to their appearance under canonical viewing conditions.