Auto-White Balance Algorithm of Skin Color Based on Asymmetric Generative Adversarial Network

Skin color constancy under nonuniform correlated color temperatures (CCT) and multiple light sources has always been a hot issue in color science. A more high-quality skin color reproduction method has broad application prospects in camera photography, face recognition, and other fields. The processing process from the 14bit or 16bit RAW pictures taken by the camera to the final output of 8bit JPG pictures is called the image processing pipeline, in which the steps of the auto-white balance algorithm have a decisive impact on the skin color reproduction result. The traditional automatic white balance algorithm is based on hypothetical statistics. Moreover, the estimated illuminant color is obtained through illuminant estimation. However, the traditional grayscale world, perfect reflector, and other auto-white balance algorithms perform unsatisfactorily under non-uniform or complex light sources. The method based on sample statistics proposes a new solution to this problem from another aspect. The deep learning algorithm, especially the generative adversarial network (GAN) algorithm, is very suitable for establishing the mapping between pictures and has an excellent performance in the fields of image reconstruction, image translation, defogging, and coloring. This paper proposes a new solution to this problem. The asymmetric UNet3+ shape generator integrates better global and local information to obtain a more refined correction matrix incorporating details of the whole image. The discriminator is Patch-discriminator, which focuses more on image details by changing the attention field. The dataset used in this article is the Liverpool-Leeds Skin-color Database (LLSD) and some supplementary images, including the skin color of more than 960 subjects under D65 and different light sources. Finally, we calculate the CIEDE2000 color difference and some other image quality index between the test skin color JPEG picture corrected by the auto-white balance algorithm and the skin color under the corresponding D65 to evaluate the effect of white balance correction. The results show that the asymmetric GAN algorithm proposed in this paper can bring higher quality skin color reproduction results than the traditional auto-white balance algorithm and existing deep learning WB algorithm.