Abstract
Human hair colorization, concerning given model hair image without changing neither hairstyle nor hair texture, is a challenging task. The principle problem making this task complicated is the difference in the texture and the illumination between a user and model images. The natural human hair consists of a mix of hair swatches. Each swatch has its chromaticity distribution, which, generally, is non-Gaussian. These swatches can be determined as color clusters in the hair image. In this case, the problem can be solved by matching between the user and the model hair swatches or color clusters. After this matching, the color transfer between the relevant model and user swatches is applied. Besides, the model’s hair should be compressed to a reasonable size to provide simultaneous representation for a variety of hair colors. The model’s hair colors are taken from the images of hair color packs usually available in decorative cosmetic stores. These images, however, are taken in standard illumination condition, so appropriate relighting should be applied to provide photorealistic user’s image. Experimental results with 530 different color models, and more than 20,000 users show that the proposed technique achieves high photorealistic perception and a reasonable compression ratio.

Keywords
Color measurement, Image color analysis, Chromaticity distribution, Probability distributions matching

Cite this paper
Uri Lipowezky, Automatic Hair Colorization and Relighting Using Chroma-ticity Distribution Matching , SCIREA Journal of Electrical Engineering. Volume 5, Issue 2, April 2020 | PP. 20-45.

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