A method for hand detection using internal features and active boosting-based learning

Hand posture recognition has important applications in sign language, human machine interface, etc. In most such systems, the first and important step is hand detection. This paper presents a hand detection method based on internal features in an active boosting-based learning framework. The use of efficient Haar-like, local binary pattern and local orientation histogram as internal features allows fast computation of informative hand features for dealing with a great variety of hand appearances without background interference. Interactive boosting-based on-line learning allows efficiently training and improvement for the detector. Experimental results show that the proposed method outperforms the conventional methods on video data with complex background while using a smaller number of training samples. The proposed method is reliable for hand detection in the hand posture recognition system.