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In order to implement watermarking scheme for embedded systems, we present an energy efficient and robust watermarking mechanism based on DS-CDMA. The DS-CDMA based watermarking can obtain the capacity according to the orthogonal spreading codes. The high capacity can improve the robustness efficiently. For power saving, we propose a low complexity frequency domain transform for the DS-CDMA embedding method. The experimental results show that our approach not only reduces 82.19% energy consumption, but can survive from various signal distortions such as JPEG compression, sharpening, blurring, intensity adjustment, and cropping.

Skin color is an important feature for face detection and recognition in color images. In order to obtain the possible face regions in color images, the skin color models are always constructed by statistical analysis. Owing to low accuracy of the static models, researches have discussed several dynamic models to correct input image such as illumination compensation, white balance, edge points addition, etc. Unfortunately, it is possible that some objects whose color is the same as the definition exist, and the previous methods can not separate real skin item from skin color background. Thus, to enhance skin color separation, this paper presents a honeycomb model to recognize the real human skin and the skin color items. First, the possible skin color is estimated from the pixels of database, and the honeycomb structure is built in HSV color space according to the training samples. Then, the personal skin is captured in one of the honeycomb cells. The performance of the new skin color detector technique has been tested under complex lighting source and background environments. It is observed that the proposed model can effectively improve the segmentation results. Especially, the honeycomb model is capable of separating the human face which connected with other face or skin color background.

Skin colour is an important feature for face detection and recognition in colour images. To obtain the possible face regions in colour images, skin colour models are always constructed by statistical analysis. Owing to low accuracy of the static models, researchers have discussed several dynamic models to correct input images. Unfortunately, it is possible that some objects whose colour is the same as the definition exist, and the previous methods cannot separate real skin item from skin colour background. Thus, this paper presents a honeycomb model to recognise the real human skin colour. The performance of the new skin colour detector technique has been tested under complex lighting source and background environments. It is observed that the proposed model can effectively improve the segmentation results. Especially, the honeycomb model is capable of separating the human face which connected with other face or skin colour background.