Skin detection in video under uncontrolled illumination

Many vision-based human-computer interaction (HCI) applications require skin detection. However, their performance relies on accuracy in detecting skin regions in video, which is difficult under uncontrolled illumination. The chromatic appearance of skin changes because of shading, often caused by body movement. To address this, we propose a dynamic adaptation method to detect skin regions affected by local color deformations. Static and dynamic skin regions are detected by a corresponding module. The static module includes a facial skin distribution model (FSDM) and a fusion-based background distribution model (FBDM). The FBDM is obtained from a local background distribution model (LBDM) and a global background distribution model (GBDM). The LBDM is obtained by comparing a frame pixel distribution model with the FSDM and GBDM. Next, the FBDM is derived from the LBDM and the GBDM. The dynamic module includes a moving skin distribution model (MSDM), derived from a set of moving skin samples. Initially, moving skin regions are detected using a modified double frame-difference method and then modeled using a Gaussian mixture model. To avoid misidentifying background regions as skin, the final MSDM is obtained by comparing the initial moving skin model to the FSDM and FBDM. Finally, the static and the dynamic models are fused by applying a maximization rule. Experimental results shows that the proposed method can detect skin regions more accurately than state-of-the-art methods.