Image specific discriminative feature extraction for skin segmentation

In recent times, the majority of colour-based skin detection methods used skin modelling in different colour spaces, and they are capable of skin classification at a pixel level. However, the accuracy of these methods is significantly affected by different issues, such as the presence of skin-like colours in scene background, variations in skin pigmentation, scene illumination, etc. Recent developments show that the discriminating power of a colour-based skin classifier can be increased by employing texture and spatial features. However, we observed that discriminability between skin and non-skin regions does not follow any statistics, and the discrimination is extremely image specific. In this paper, a novel adaptive discriminative analysis (ADA) is proposed to extract most discriminant features between skin and non-skin regions from an image itself in an unsupervised manner. Experimental results for standard databases show that the proposed method can efficiently segment out skin pixels in the presence of skin-like background colours.