Adaptive skin detector enhanced with blob analysis for gesture recognition

This paper presents a novel blob analysis algorithm which significantly improves skin detection effectiveness. In our research we developed an adaptive skin detector which utilizes statistical skin color model updated dynamically based on a face region. Faces are detected in luminance channel based on their geometric properties. Color-based skin detectors classify every pixel separately which results in high false positives for background pixels which color is similar to human skin. The proposed blob analysis technique verifies detected skin regions by taking into account pixel topology. The experiments for ECU database showed that with the proposed approach false positive rate was reduced from 15.6% to 6% compared with a statistical model in RGB.