Skin color model adaptation under varying lighting conditions

Abstract

Skin region detection is crucial for face recognition, hand tracking, and motion detection. In the detection process, a skin color model is usually required to confine the distribution of skin colors. However, skin color models are sensitive to lighting conditions. Skin segmentation under varying lighting conditions produces poor results. This article presents a skin detection procedure for human–computer interaction sessions under varying lighting conditions. The proposed method requests a skin sample from the user to estimate the color temperature of the light source. Then, the color temperature is used to correct the skin sample. At the subsequent step, the mean of the corrected skin sample is utilized to adapt the skin color model. Finally, the adapted skin color model is employed to segment skin regions in the video stream. Tests using the proposed method and some adaptive skin detection algorithms have been conducted. Statistical data show that the proposed method is superior to color constancy methods and the Gaussian mixture model in skin region segmentation. The proposed method improves the true positive rate by more than 13% in segmenting skin regions of a database. Its true positive rate is 20% better if real-life images are used as test data.