Aiming at the problem that the robustness of gesture recognition is difficult to guarantee, this paper presents a method based on multi-features and sparse representation. Hu invariant moments and HOG features of training samples are extracted in training phase. The K-SVD algorithm is used to train the initial value of dictionary formed by two features so as to obtain two sub-dictionaries. In recognition phase, sparse coefficients of corresponding training dictionary are derived by solving minimum l 1-norm. Finally, the overall reconstruction error is calculated to judge the categories of test samples. In experimental simulation, five kinds of grasp gesture are collected to create gesture sample library. After selecting optimal HOG parameters and the weight of two features, the recognition effect of the method is analysed. Compared with the commonly used classification, the results show that the method has better recognition rate and robustness.

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Aiming at the problem that the robustness of gesture recognition is difficult to guarantee, this paper presents a method based on multi-features and sparse representation. Hu invariant moments and HOG features of training samples are extracted in training phase. The K-SVD algorithm is used to train the initial value of dictionary formed by two features so as to obtain two sub-dictionaries. In recognition phase, sparse coefficients of corresponding training dictionary are derived by solving minimum l 1 -norm. Finally, the overall reconstruction error is calculated to judge the categories of test samples. In experimental simulation, five kinds of grasp gesture are collected to create gesture sample library. After selecting optimal HOG parameters and the weight of two features, the recognition effect of the method is analysed. Compared with the commonly used classification, the results show that the method has better recognition rate and robustness.