Improved Automatic Skin Detection in Color Images

Mahalanobis distance has already proved its strength in human skin detection using a set of skin values. We present this work that uses automatic skin detection after an initial camera calibration. The calibration is done by human sampling from test individuals. A scaling is performed on the work data, before applying the Mahalanobis distance that ensures better results than previous works. We use the TSL color space also used successfully by others authors, where undesired effects are reduced and the skin distribution fits better in a Gaussian model than in others color spaces. Also, using an initial filter, normally large areas of easily distinct non skin pixels, are eliminated from further processing. Analyzing and grouping the resulting elements from the discriminator, improves the ratio of correct detection and reduce the small non skin areas present in a common complex image background, including Asiatic, Caucasian, African and interracial descent persons. Also this method is not restricted to orientation, size or grouping candidates. The present work is a first step in a approach for human face detection in color images, but not limited in any way to this goal.