Hand Posture Recognition Using SURF with Adaptive Boosting

An approach making use of SURF feature and Adaboost for hand posture recognition is proposed. First the SURF key points are extracted to describe the blob or ridge-like structures from grey level images. These are potential points of interest that can be used to match with other images with similar structures. Then the statistic parameters of the tendency of gradient changes within small patches surrounding the points of interest are calculated as feature vectors. With all the points of interest, Adaboost is used to train a strong classifier for each posture by selecting the most efficient features, which largely lowers the computational cost of the classification stage. The proposed method was tested on the Triesch Hand Posture Database which is the benchmark in the field. Experimental results showed that our method outperforms existing methods in terms of better recognition accuracy.