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Human hand detection problem has important applications in sign language and human machine interfaces. In this work, we present a novel approach for learning a vision-based hand detection system. The main contribution is a robust on-line boosting-based framework for real-time detection of a hand in unconstrained environments. The use of efficient representative features allows fast computation while dealing with vast changing of hand appearances and background. Interactive on-line training allows efficiently train and improve the detector. Moreover, we propose a strategy to efficiently improve the performance meanwhile reduce hand labeling effort. Besides, if necessary, we use a verification process to prevent ldquodriftingrdquo of classifier over time. The proposed method is practically favorable as it meets the requirements of real-time performance, accuracy and robustness. It works well with reasonable amount of training samples and is computational efficient. Experiments for detection of hands in challenging data sets show the outperform of our approach.