Background: Accurate skin colour measurements are important for numerous medical applications including the diagnosis and treatment of cutaneous disorders and the provision of maxillofacial soft tissue prostheses.Methods: In this study, we obtained accurate skin colour measurements from four different ethnic groups (Caucasian, Chinese, Kurdish, Thai) and at four different body locations (Forehead, cheek, inner arm, back of hand) with a view of establishing a new skin colour database for medical and cosmetic applications. Skin colours are measured using a spectrophotometer and converted to a device‐independent standard colour appearance space (CIELAB) where skin colour is expressed as values along the three dimensions: Lightness L*, Redness a* and Yellowness b*. Skin colour differences and variation are then evaluated as a function of ethnicity and body location.Results: We report three main results: (1) When plotted in a standard colour appearance space (CIELAB), skin colour distributions for the four ethnic groups overlap significantly, although there are systematic mean differences. Between ethnicities, the most significant skin colour differences occur along the yellowness dimension, with Thai skin exhibiting the highest yellowness (b*) value and Caucasian skin the lowest value. Facial redness (a*) is invariant across the four ethnic groups. (2) Between different body locations, there are significant variations in redness (a*), with the forehead showing the highest redness value and the inner arm the lowest. (3) The colour gamut is smallest in the Chinese sample and largest in the Caucasian sample, with the Chinese gamut lying entirely the Caucasian gamut. Similarly, the largest variability in skin tones is found in the Caucasian group, and the smallest in the Chinese group.Conclusion: Broadly speaking, skin colour variation can be explained by two main factors: individual differences in lightness and yellowness are mostly due to ethnicity, whereas differences in redness are primarily due to different body locations. Variations in lightness are more idiosyncratic probably reflecting the large influence of environmental factors such as exposure to sun.

For the correlated color temperature (CCT) of a light source to be estimated, a nonlinear optimization problem must be solved. In all previous methods available to compute CCT, the objective function has only been approximated, and their predictions have achieved limited accuracy. For example, different unacceptable CCT values have been predicted for light sources located on the same isotemperature line. In this paper, we propose to compute CCT using the Newton method, which requires the first and second derivatives of the objective function. Following the current recommendation by the International Commission on Illumination (CIE) for the computation of tristimulus values (summations at 1 nm steps from 360 nm to 830 nm), the objective function and its first and second derivatives are explicitly given and used in our computations. Comprehensive tests demonstrate that the proposed method, together with an initial estimation of CCT using Robertson’s method [J. Opt. Soc. Am . 58, 1528-1535 (1968)], gives highly accurate predictions below 0.0012 K for light sources with CCTs ranging from 500 K to 10⁶ K.

A improved spectral reflectance reconstruction method is developed to transform camera RGB to spectral reflectance for skin images. Rather than using conventional direct or two-step processes, we transform camera RGB to skin reflectance directly using a principal component analysis (PCA) approach. The novelty in our direct method (RGB to spectra) is the use of a skin-specific colour characterisation chart with spectra closer to human skin spectra, and a new database of skin reflectances to derive the PCA bases. The experimental results using the facial images of 17 subjects demonstrate that our new direct method gives a significantly better performance than conventional, two-step methods and direct methods with traditional characterization charts. This new spectral reconstruction algorithm is sufficiently precise to reconstruct spectral properites relating to chromophores and its performance is within the acceptable range for maxillofacial soft tissue prostheses (error < 3 ΔE*_ab units).

Colour preference for lighting is generally influenced by three kinds of contextual factors, the light, the object and the observer. In this study, a series of psychophysical experiments were conducted to investigate and compare the effect of certain factors on colour preference, including spectral power distribution of light, lighting application, observers’ personal colour preference, regional cultural difference and gender difference. LED lights with different correlated colour temperatures were used to illuminate a wide selection of objects. Participant response was quantified by a 7-point rating method or a 5-level ranking method. It was found that the preferred illumination for different objects exhibited a similar trend and that the influence of light was significantly stronger than that of other factors. Therefore, we conclude that the light itself (rather than, e.g. the objects that are viewed) is the most crucial factor for predicting which light, among several candidates with different correlated colour temperatures, an observer will prefer. In addition, some of the gamut-based colour quality metrics correlated well with the participants’ response, which corroborates the view that colour preference is strongly influenced by colour saturation. The familiarity of the object affects the ratings for each experiment while the colour of the objects also influences colour preference.

An improved spectral reflectance estimation method is developed to transform raw camera RGB responses to spectral reflectance. The novelty of our method is to apply a local weighted linear regression model for spectral reflectance estimation and construct the weighting matrix using a Gaussian function in CIELAB uniform color space. The proposed method was tested using both a standard color chart and a set of textile samples, with a digital RGB camera and by ten times ten-fold cross-validation. The results demonstrate that our method gives the best accuracy in estimating both the spectral reflectance and the colorimetric values in comparison with existing methods.