Abstract The learning method for hand gesture recognition system based on vision is commonly offline, which results in repeated offline learning when new hand gestures come. Its realtime performance, expansibility and robustness are poor. In this paper, a method named online PCA with adaptive subspace is proposed for hand gesture recognition. The subspace is updated online by calculate PCA of sample coefficients. The subspace updating strategy is adjusted according to the difference degree between new sample and learned sample. The algorithm is able to adapt different situations and reduce the cost of oalculation and storage. The incrementally online learming and recognition of hard gestures are realized by the proposed algorithm. Experimental results show that the proposed method solves the unknown hand gesture problem, realizes online hand gesture accumulation and updating and improves the recognition performance of system.
2011, Vol. 24 
