Color-based and context-aware skin detection for online video annotation

By analyzing the low level features of images only, skin detection in visual data cannot be solved. To compensate for this major drawback of many approaches, we combine a state of the art recognition algorithm with color model based skin detection. Detected faces in videos are the basis for adaptive skin-color models, which are propagated throughout the video, providing a more precise and accurate model in its recognition performance than pure color based approaches. The approach is able to run in real-time and does not need prior data-specific training. We received challenging online videos from an online service provider and use additional videos from public Web platforms covering a grand variety of different skin-colors, illumination circumstances, image quality and difficulty levels. In an extensive evaluation we estimated the best performing parameters and decide on the best model propagation techniques. We show that adaptive model propagation outperforms static low level detection.