In this paper we present a new approach to real-time and rotation invariant hand pose detection, which is based on a novel technique for computing the best hand skin profile. This skin profile is used to classify each pixel in the current video frame as belonging to the skin color or to the background and corresponds to a group of 3D line segments (vectors), where the control points are important HSV (Hue-Saturation- Value) 3D coordinates extracted during the skin capture stage. The runtime pixel classification is evaluated by measuring the distance of each pixel HSV 3D cylindrical coordinates to each one of formed vectors of the current skin profile. A space transformation, from HSV cone to HSV 3D cylindrical coordinates, is performed due to the Hue component discontinuity around the 360º, found in the HSV model, which would prevent any direct arithmetic comparison between Hue values. After skin/background segmentation, we construct efficient and reliable scale and rotation invariant hand pose gesture descriptors, by introducing an innovative technique, referred to as “oriented gesture descriptors”. These descriptors correspond to grayscale image representations of the hand gesture captured during gesture acquisition. Finally, hand pose recognition is computed using a template matching technique, which is light invariant [BD05], between the acquired gestures/descriptors and the current tracking gesture. The system takes into account the fact that a moving hand, in a dynamic light environment, can present several variations of the predominant skin-tone, instead of just using a single color tone as a reference, such as in [MOC06].

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• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology