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

Abstract

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.