2.5. Studies of skin colours at different spaces

Because of increasing interest in faces, there have been studies on behaviour of skin chromaticities at different colour spaces. Many studies have indicated that the skin tones differ mainly in their intensity value while they are very similar in chrominance coordinates, see for example Graf et al. (1996), Yang and Waibel (1996), Graf et al. (1995), and Hunke and Waibel (1994). Terrillon et al. (2000) evaluated both different chrominance spaces and skin colour distribution models. They use a single Gaussian and Gaussian mixtures for modelling skin chromaticity distributions in nine colour spaces (TSL, NCC rgb, CIE xy, CIE SH, HSV, YIQ, YES, CIE Luv and CIE Lab). (For other than CIE colour spaces(Wyszecki & Stiles 2000), see Appendix 1). The images used in the evaluation were taken under slowly varying illumination conditions under one camera or downloaded from the Internet. This most probably means that their study considered only skin colours obtained under white balanced or near white balanced conditions. According to their research, for a single Gaussian model the best results were obtained in illumination normalized colour spaces, whereas the use of Gaussian mixture models improved results with those colour spaces which do not use illumination normalization. The use of Gaussian mixture in an illumination normalized colour space produced comparable results to a single Gaussian model. They found that skin colour distribution in a space with no illumination normalization is complex shaped. The normalization produced distributions which were simpler to model, confined and more efficient for skin colour segmentation. An interesting observation was made on the behaviour of HSV space: the saturation S is sensitive to skin colour and it took almost all values for a limited hue H range. An illumination normalized colour space, TSL, was developed and then produced better performance. In their paper, they also presented a technique for calculating the threshold based on true positives and true negatives. Later, Terrillon et al. (2001) found that NCC rgb and CIE xy were most efficient for skin segmentation and these spaces produced the smallest area for skin chromaticities. They also tested portability of colour spaces between two cameras and concluded that the most portable was CIE xy and then NCC rgb. These two spaces were confirmed again to be best fitting for a single Gaussian colour model and most effective for face detection. NCC rgb had the highest correct face detection rate and correct nonface rejection rate.

Zarit et al. (1999) compare five colour spaces for classification of skin pixels in a colour histogram based applications. The colour spaces were CIE Lab, Fleck HS, HSV, Normalized RGB and YCrCb. The colour histogram based methods were based on a look-up table and Bayesian decision theory. Most of the images in their study were downloaded from the Internet, which means that the images do most probably contain very much shifting of chromaticities of skin tones. They found that for the look-up table method, the HS-spaces performed best while the CIE Lab and YCbCr were poorer. For Bayesian decision based classification, the choice of colour space did not matter but the maximum likelihood method produced better results than the maximum a posteriori method.

Three colour spaces, RGB, YUV and HSV, were evaluated for PCA based face recognition by Torres et al. (1999). According to them, RGB and luminance Y produced equal recognition rates, but better performance was obtained with SV components and YUV space. However, the skin appearance did not have very many colour shifts between the test image and found match image, and in all images shown faces and other skin objects seem to have skin tone or near skin tones colour appearance.

However, these studies have not considered so much colour shifts from skin tones because they do not address clearly real illumination changes. They do not specify under which camera white balancing and prevailing illumination conditions the images were taken, although this might be difficult for images downloaded from the Internet. It is therefore necessary to make a study about the behaviour of skin colours under defined camera white balancing and prevailing illumination conditions for different colour spaces.