Facial color management for mobile health in the wild

Nowadays, mobile technologies have changed the patient routine health care and management. With a large amount of mobile health applications developed, massive and valuable health data are possibly collected with a smart mobile phone in hand. Facial color images are recently proved to be available and effective for health condition diagnosis both in modern medicine and ancient medicine perspectives. One significant issue of facial color health condition diagnosis system is color management, in which its primary procedure is to obtain reliable and device-independent facial color images in the wild. The solution is known as utilizing color correction technology to recover the intrinsic color properties of facial images. However, current color correction approaches are hard to meet the need of mobile health management in the wild, due to some limitations of precision-challenged algorithm, inconvenient color imaging device, too strong scenario assumption and so forth. Therefore, in this paper, we propose a novel facial color correction framework to realize the facial color management of mobile health in the wild. Our approach mainly focuses on providing the solution to three problems: nonuniform light normalization, facial color gamut related color patches selection and the color correction model decision optimization. Experimental results with qualitative and quantitative assessments on the indoor and outdoor scenarios validate that the proposed approach is more outstanding than our previous method and color constancy methods.