An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach

Abstract Human hand detection and segmentation plays an important role in sign language recognition and human machine interaction. In this paper, a novel approach for learning a vision-based hand detection system is introduced. The main contribution of this paper includes robust boosting-based framework for real-time detection of a hand in unconstrained and heterogeneous environments. The proposed system makes use of efficient representative features which allows fast computation while changing the hand appearances and background. Moreover, this proposed strategy efficiently improves the performance while reducing the effort of hand labeling. Experimental results show that the proposed method is practically more flattering as it meets the requirements of real-time performance, accuracy and robustness. This system has been proved to work well with a reasonable amount of training samples and was computationally found to be more effective and efficient.