Abstract
Abstract Various approaches of skin detection have yet to demonstrate a stable state of performance. This is due to skin color in an image that is sensitive to variant illumination, camera adjustments, and human skin types. To contribute in overcome this problem a robust skin detection method that integrates both color and texture features is proposed. Texture features were estimated using statistical measures as range, standard deviation, and entropy. Back-propagation artificial neural network is then used to learn features and classify any given inputs. In this work, two skin detectors based on texture features only, and a combination of both color and texture features (proposed) have been constructed. Furthermore, the paper analyzes and compares the obtained results from the both skin detectors to show the impact of the integrating color and texture features to the robustness level. It found that the proposed skin detection method achieved a true positive rate of approximately 94.5% and a false positive rate of approximately 0.89%. Experimental results showed that proposed approach is more efficient compared with other state-of-the-art texture-based skin detector approaches.
