User identification opens up new interaction possibilities on interactive surfaces. Yet many current multi-touch systems only detect isolated touches and cannot identify users. This paper presents a low-cost, biometric method for user identification for vision-based interactive surfaces. To identify users, we extract characteristic contour features from a flat hand posture and use Support Vector Machines (SVM) for classification. Our evaluation shows the method’s robustness together with high true and low false positive rates of 96% respectively 0.5%. We further outline possibilities to integrate this method with surface interaction techniques, taking into account that users have to perform distinctive hand postures to afford identification.
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