Colour cameras, video cameras and their applications have become increasingly popular among professionals and amateurs alike. Still, many colour related problems have not yet vanished, like problems of a colour camera keeping stable colour appearance for an object or producing similar colour appearances as the human vision system. To make the situation more difficult, different colour cameras do not necessarily produce the same colour appearances for the same scene under the same imaging conditions. One of the main reasons for different appearances is in the first stage of image formation: spectral sensitivities of the sensors diverge from those of the human eye and from the other cameras. Of course, there are cameras with responses similar to the human eyes, but at least so far they are rarely used today.
One of the remarkable things in the human vision system is its ability to disregard the effects of widely varying illumination, automatically. This ability aids in keeping the object’s colour appearance stable, and it is often erroneously called colour constancy which is only approximately true. In the literature, it has been claimed to be both a high level brain process (which contains, among other things, a memory for some colours, and adjustment for lighting level) or a low level process. The details behind the colour constancy mechanism are still under research, although many theories and studies have been suggested, but they are beyond the scope of this thesis.
Unfortunately, colour cameras themselves do not have this kind of “built-in” mechanism against illumination dependency. They cannot separate changes in an object’s reflectance from changes in illumination over the object. The proper white balancing or white calibration of the camera to the prevailing light source does not guarantee any other colour than the “white” calibration object having the same colour appearance in images taken under different light sources. The problem worsens when the illumination changes from the calibrated cases: distortion can appear in objects’ colours (both in intensity and in chromaticity) due to the illumination variation and the properties of cameras, like limited dynamic range.
Problems caused by illumination in colour imaging are handled in general in four different manners: 1) preventing changes by controlling illumination or ignoring information taken under changed condition, 2) using a process which disregards illumination, 3) adapting to the changes or 4) combining the second and the third to improve robustness. The first possibility is inadequate in many applications because it is impossible to control illumination in many real world situations and ignoring information may lead to a loss of essential data. The second option is to use illumination invariant (or robust) features or colour correction, in other words, colour constancy for cameras. Illumination invariance here means invariance / robustness towards lighting with different spectra and intensity although in some cases it has been used to stand for invariance to the direction of a light source. The goal of colour correction is usually correction of chromaticities back to the original values, while the invariant features try to present colour information independent of lighting conditions. A massive number of papers have been published in this area, but still for machine vision applications their performance is not necessarily enough. For example, some colour cameras do have an automatic colour correction method like the grey world algorithm (Buchsbaum 1980) and these methods can produce satisfactory results at least for a human observer as long as the assumptions and constraints imposed by the methods are valid. But in many scenes, the results are poor even for human evaluation and it is very easy to show that these algorithms fail. In fact, the correction can lead to unstable colour appearance and wrongly corrected colours. Almost all correction algorithms except Retinex (Land 1977, Land 1986, and Land & McCann 1971) work under one global illumination change whereas in practise, local changes are common. There have been anyway suggested methods for correcting nonuniform intensity (Chang & Reid 1996, Powell et al. 1999) but this can be also cancelled by using only chromaticities. Illumination invariant features can be pixel based or region based but they are not successful either for the same reasons as the colour correction algorithms. In an extreme case, these features are obtained by quantization to a few possible colour values (Redfield & Harris 2000). This causes poor discrimination capability and is therefore useful only in a couple of applications. In general, once the illumination has changed and sensor readings obtained, it is impossible to reconstruct the ideal values due to information losses introduced by the change. The third option is investigated in this thesis whereas the fourth option will be hopefully studied in the future.
In this thesis, the adaptive schema are studied with colour images or frames of human faces and facial skin colour because a practical solution for realistic illumination problems is being sought for machine vision purposes. Also a colour correction schema for facial colours is presented under severe information loss due to clipping. Faces are selected as the study target since they are common and important objects in videos and images. But what is skin colour? Although the answer to this question might seem trivial - perceived colour appearance of skin - a closer look at it reveals an interesting dependence on the perceiver. The human perceiver usually sees the skin colour as quite constant and stable over a wide range of illumination conditions. The skin chromaticities observed are few and are located in a limited region in the chromaticity space. In fact, humans can easily notice even a small deviation from these chromaticities and therefore it is important to have a high quality representation of skin colour (Harwood 1976, Satyanarayana & Dalal 1996 and Lee & Ha 1997). On the other hand, uncalibrated cameras can produce a rainbow colour appearance for skin under illumination conditions varying between sunset / sunrise and daylight because of the lack of a colour constancy ability. The possible skin chromaticities for the camera cover a large region in a chromaticity space. This skin chromaticity region can be reduced drastically by white balancing the camera properly each time for the prevailing illumination. Although often unspecified in the literature, in this thesis, the skin colour refers to all possible perceivable chromaticities of skin. The term skin tone is used to refer to cases with a smaller skin chromaticity area and shades generally associated with proper skin colour by humans.