This research examined the performance of skin coloredpatches for accurately estimating human skin color. More than 300 facial images of Korean females were taken with a digital singlelens reflex camera (Canon 550D) while each was holding the X-Rite Digital ColorChecker® semi-gloss target. The color checker consisted of 140 color patches, including the 14 skin-colored ones. As the ground truth, the CIE 1976 L*a* b* values of seven spots in each face were measured with a spectrophotometer. For an examination, three sets of calibration targets were compared, and each set consisted of the whole 140 patches, 24 standard color patches and 14 skin-colored patches. Consequently, three sets of estimated skin colors were obtained, and the errors from the ground truth were calculated through the square root of the sum of squared differences (ΔE). The results show that the error of color correction using the 14 skin-colored patches was significantly smaller (average ΔE = 8.58, SD = 3.89) than errors of correction using the other two sets of color patches. The study provides evidence that the skin-colored patches support more accurate estimations of skin colors. It is expected that the skin-colored patches will perform as a new standard calibration target for skin-related image calibration.
This research suggests a color constancy algorithm using the human sclera and pupil for estimating skin color. The human sclera is approximately white, reflecting the hue characteristics of the illuminant. On the contrary, the pupil is shown as approximately black, independently of the surroundings. Consequently, the sclera and pupil account for the color characteristics of a facial image. Based on this assumption, a new color constancy algorithm was developed, and we examined the performance by comparing the calibrated colors with actual skin colors measured with a spectrophotometer. As the dataset, we collected facial images as well as the CIEL*a*b* values of 348 Korean females. As a result, the error of the proposed algorithm was significantly smaller than the state-of-art skin estimation algorithms. The algorithm developed in this study provides evidence that the human sclera and pupil successfully serve as the calibration targets to estimate the color of human skin.
This paper presents a novel joint geometric camera calibration system using a novel calibration target for a visible and a far-infrared (FIR) cameras. By using the proposed calibration target which is the two-layer structure with different combinations of thermal emission, we can stably and precisely obtain the corresponding points of the checker pattern in the calibration target from the visible and the FIR images. The simple calibration algorithm based on the well-known Zhang's algorithm can accurately estimate the camera parameters, because we can use many useful tools which contribute the accuracy. Experimental results show that the proposed calibration system enables us to easily calibrate the visible and the FIR image with high accuracy compared with an existing system. Furthermore, the proposed system can lead to develop various applications including joint image denoising, and joint image up-sampling.