Illuminant color estimation based on pigmentation separation from human skin color

Quantitative analysis of human skin color is needed in the medical and cosmetic fields. Because of the strong light scattering by biological tissues, however, analysis of skin color has not yet been fully successful. Human skin color is dominated by the colors of blood and melanin which are modified by scattering. Exposure of human skin to hot water or UV-ray changes its color because the absorbance spectrum of reflection from the human skin, and the changes in the absorbance spectra of reflection reflect the absorbance spectra of blood and melanin which are the absorption spectrum distorted by scattering. By applying the modified Lambert-Beer law, the absorbance spectrum of reflection from human skin can be expressed proportional to those distorted absorbance spectra of blood and melanin. Multiple linear regression analysis is successfully used to reproduce the absorbance spectrum of reflection from human skin from the distorted absorbance spectra of blood and melanin.

Selection for gamut mapping colour constancy

The research on facial animation is growing and became more sensible in terms of three dimensional data. The emergence of laser scans and advanced 3D tools imparted further towards the rapid development of complex facial model. This paper has given an overview on facial animation techniques such as blend shape interpolation, facial action coding system and parameterization. The major challenges, excitement, applications, recent progress and the future of various diversified facial animation methods are high-lighted as well. The emotional theory, involving the cognition as one of the elements of emotion and emphasizing on somatic factors describing emotional expressions and its perception as the other elements are presented. The essential factors responsible for emo-tional colors manifestations to an individual are the key issues. The notable theories and models of Goethe, Claudia Cortes, Naz Kaya, Color Wheel Pro, Shirley Willet and Yau Xueon facial colors expressions are critically commented and a facile comparison is car-ried out. Furthermore, a detailed discussions on facial colors appearance are provided using image based skin color analysis and synthesis, reflectance based model, bidirec-tional texture function, physiology measurement based and emotional appearance mod-els. The paper completed with the remarkable features official expression comprised of facial action coding system and MPEG-4 facial animation that enable considerable im-provement on facial animation technology. Our in-depth appraisal on facial animation may contribute as taxonomy towards the development of human facial skin colors on 3D avatar including facial action coding system, emotion theory, color theory, animation via blending and image based color analysis useful for various applications.

Skin color is an important physiological index of human skin that has essential significance in cosmetology and dermatological diagnoses and aesthetics, especially in East Asian culture, where the beauty standard is the whiter the skin, the more beautiful a person is. However, there has been a lack of objective comparisons of facial skin color in East Asian females. Skin color at the cheek site was measured in 445 healthy females, including 362 Chinese women in Beijing and 83 Japanese women in Tokyo, all aged 18-50years. The parameters consisted of L* , a* , b* , individual typology angle (ITA°), and hue angle and were determined using noninvasive skin measurement instruments. The skin color categories in East Asians ranged from very light (I) to brown (V), and the dark (VI) category was not observed; the main categories were light (II), intermediate (III), and tan (IV). The facial skin color of Chinese individuals was brighter, more reddish, and less yellowish than that of Japanese individuals. With age, L* values decreased, and a* values and hue angle increased in East Asians with significant differences. Chinese women showed the same differences as Japanese women for all parameters in all age groups. This study provides objective skin color measurements and examines significant differences with respect to geographic location and age.

The White-Patch method, one of the very first colour constancy methods, estimates the illuminant colour from the maximum response of three colour channels. However, this simple method has been superseded by advanced physical, statistical and learning based colour constancy methods. Recently, a few research works have suggested that the simple idea of using maximum pixel values is not as limited an idea as it seems on first glance. These works show that in several situations some manipulations can indeed made it perform very well. Here, we extend the White-Patch assumption to include any of: white patch, highlights or light source; let us refer to these pixels in an image as the “bright” pixels areas. We propose that bright pixels are surprisingly helpful in the illumination estimation process.In this paper, we investigate the effects of bright pixels on several current colour constancy algorithms. Moreover, we describe a simple framework for an illumination estimation method based on bright pixels and compare its accuracy to well-known colour constancy algorithms applied to four standard datasets. We also investigate failure and success cases, using bright pixels, and propose desiderata on input images with regard to the proposed method.

A Chinese skin colour database is established based on measurements taken from nine bodies areas of 202 Chinese individuals to move towards accurate skin colour reproduction. The colour appearance of each skin point is predicted, and a comprehensive colour gamut for Chinese skin is determined. A consistent colour shift between facial and arm colours is identified, in which facial colour tends to be more reddish, more colourful, and darker than arm colour. Moreover, Chinese females are found to have lighter, paler, and more yellowish skin than Chinese males. Variations in Chinese skin colour are quantified, and body area differences and gender differences are shown to have significant effects on Chinese skin colour.

In this paper, we propose perceptually optimized enhancement of contrast and color in images using just-noticeable-difference (JND) transform and color constancy. We adopt JND transform to get JND map that represents the perceptual response of the human visual system (HVS). We utilize color constancy to estimate the light source color and be robust to color bias. First, we use a perceptual generalized equalization model for the optimization of both color and contrast based on color constancy and contrast enhancement, i.e. base image. Second, we generate JND map based on HVS response model from foreground and background luminance, called JND transform. Next, we update the JND map based on Weber’s law to boost perceptual response. Finally, we perform inverse JND transform from the base image and its JND map to produce the enhanced image highly correlated with the human visual perception. Experimental results show that the proposed method achieves good performance in contrast enhancement, color reproduction, and detail enhancement.

Using convolutional neural network models illumination estimation according to light colors

Dermoscopy images classification is the major method for melanoma detection. Currently, the most effective method of image classification is deep learning. However, training a deep neural network requires huge amounts of training data, and it is difficult to collect enough dermoscopy images. In this paper, we introduce the deep attention network which is optimized by attention mechanism and fisher criterion. Experiment results showed that this hybrid neural network improved the recognition rate when sample size was insufficient. Besides, considering the color of dermoscopy image is an important factor for melanoma detection, and the illumination may alter the color of dermoscopy image. We researched the color constancy algorithms for color calibration and tested the impact of color constancy on our model. The result proved that color constancy can further improve the recognition rate.

Color constancy is the ability to measure image features independent of the color of the scene illuminant and is an important topic in color and computer vision. As many color constancy algorithms exist, different distance measures are used to compute their accuracy. In general, these distances measures are based on mathematical principles such as the angular error and Euclidean distance. However, it is unknown to what extent these distance measures correlate to human vision.Therefore, in this paper, a taxonomy of different distance measures for color constancy algorithms is presented. The main goal is to analyze the correlation between the observed quality of the output images and the different distance measures for illuminant estimates. The output images are the resulting color corrected images using the illuminant estimates of the color constancy algorithms, and the quality of these images is determined by human observers. Distance measures are analyzed how they mimic differences in color naturalness of images as obtained by humans.Based on the theoretical and experimental results on spectral and real-world data sets, it can be concluded that the perceptual Euclidean distance (PED) with weight-coefficients (w R = 0.26, w G = 0.70, w B = 0.04) finds its roots in human vision and correlates significantly higher than all other distance measures including the angular error and Euclidean distance.KeywordsDistance MeasureColor SpaceHuman ObserverAngular ErrorHyperspectral DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper introduces a simple idea of RGB to spectral image conversion without any expensive multi-spectral devices. A spectral reflectance with the closest colorimetric value to that of RGB pixel is picked up from the spectral palette and embedded in each pixel of RGB image. SVD (Singular Value Decomposition) compresses the high-resolution spectral image to a compact data. A conventional RGB camera image is transformed into a pseudo-spectral image and facial skin color appearance is estimated under different illuminants. The paper reports a facial color reproduction experiments using skin color palette extracted from JIS spectral data base SOCS and discusses the color accuracy after SVD compression with low dimensional parameters.

BackgroundFacial appearance, whether consciously or subconsciously assessed, may affect clinical assessment and treatment strategies in the Intensive Care Unit (ICU). Nevertheless, the association between objective clinical measurement of facial appearance and multi-organ failure is currently unknown. The objective of this study was to examine whether facial appearance at admission is associated with longitudinal evaluation of multi-organ failure.MethodsThis was a sub-study of the Simple Intensive Care Studies-II, a prospective observational cohort study. All adult patients acutely admitted to the ICU between March 26, 2019, and July 10, 2019, were included. Facial appearance was assessed within three hours of ICU admission using predefined pictograms. The SOFA score was serially measured each day for the first seven days after ICU admission. The association between the extent of eye-opening and facial skin colour with longitudinal Sequential Organ Failure Assessment (SOFA) scores was investigated using generalized estimation equations.ResultsSOFA scores were measured in 228 patients. Facial appearance scored by the extent of eye-opening was associated with a higher SOFA score at admission and follow-up (unadjusted 0.7 points per step (95%CI 0.5 to 0.9)). There was no association between facial skin colour and a worse SOFA score over time. However, patients with half-open or closed eyes along with flushed skin had a lower SOFA score than patients with a pale or normal facial skin colour (P-interaction < 0.1).ConclusionsThe scoring of patients’ facial cues, primarily the extent of eye-opening and facial colour, provided valuable insights into the disease state and progression of the disease of critically ill patients. The utilization of advanced monitoring techniques that incorporate facial appearance holds promise for enhancing future intensive care support.

Colour cast is the ambient presence of unwanted colour in digital images due to the source illuminant while colour constancy is the ability to perceive colors of object, invariant to the colour of the source illuminant. Existing statistic based colour constancy methods use whole image pixel values for illuminant estimation. However, not every region of an image contains reliable colour information. Therefore, the presence of large uniform colour patches within the image considerably deteriorates the performance of colour constancy algorithms. This paper presents an algorithm to alleviate the biasing effect of the uniform colour patches of the colour constancy compensation techniques. It employs the k-means clustering algorithm to segment image areas according to their colour information. The Average Absolute Difference (AAD) of each colour component of the segment is calculated and used to identify and exclude segments with uniform colour information from being used for colour constancy adjustments. Experimental results were generated using three benchmark datasets and compared with the state of the art techniques. Results show the proposed technique outperforms existing techniques in the presence of the uniform colour patches and similar to Grey World method in the absent o uniform colour patches.

Hybrid direct combination color constancy algorithm using ensemble of classifier

Accurate facial skin colour representation is highly required for an increasing number of applications, such as the solution of cosmetic products, the diagnosis of cutaneous diseases, and the manufacture of soft tissue prostheses. This study presents a novel camera colour characterisation model with higher predictive accuracy for the image-based colour measurement of human skin. More specifically, a digital imaging system was developed to collect the facial images of sixty human subjects from four ethnic groups. The newly collected human facial skin colour data and a conventional colour chart were selected as the training dataset, respectively, and three general techniques (linear transformation, polynomial regression, and root-polynomial regression) were utilised to derive the characterisation model by mapping camera digital signals to CIE XYZ tristimulus values. The predictive accuracy of each model was then verified using the mean CIELAB colour difference between actual skin colour measurements and the corresponding predictions from colour images. Results showed that the best model performance was achieved when the human skin colours of real subjects were used as the training samples and first order polynomial regression was used as the mapping algorithm.