The biggest challenge in hand detection and tracking is the high dimensionality of the hand's kinematic configuration space of about 30 degrees of freedom, which leads to a huge variance in its projections. This makes it difficult to come to a tractable model of the hand as a whole. To overcome this problem, we suggest to concentrate on posture invariant local constraints, that exist on finger appearances. We show that, besides skin color, there is a number of additional geometric and photometric invariants. This paper presents a novel approach to real-time hand detection and tracking by selecting local regions that comply with these posture invariants. While most existing methods for hand tracking rely on a color based segmentation as a first preprocessing step, we integrate color cues at the end of our processing chain in a robust manner. We show experimentally that our approach still performs robustly above cluttered background, when using extremely low quality skin color information. With this we can avoid a user- and lighting-specific calibration of skin color before tracking.
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