Specular Highlight Removal in Facial Images

Chen Li, Stephen Lin, Kun Zhou, Katsushi Ikeuchi; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3107-3116

We present a method for removing specular highlight reflections in facial images that may contain varying illumination colors. This is accurately achieved through the use of physical and statistical properties of human skin and faces. We employ a melanin and hemoglobin based model to represent the diffuse color variations in facial skin, and utilize this model to constrain the highlight removal solution in a manner that is effective even for partially saturated pixels. The removal of highlights is further facilitated through estimation of directionally variant illumination colors over the face, which is done while taking advantage of a statistically-based approximation of facial geometry. An important practical feature of the proposed method is that the skin color model is utilized in a way that does not require color calibration of the camera. Moreover, this approach does not require assumptions commonly needed in previous highlight removal techniques, such as uniform illumination color or piecewise-constant surface colors. We validate this technique through comparisons to existing methods for removing specular highlights.