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Gesture recognition based on an improved local sparse representation classification algorithm

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Abstract

The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized \(l_{1}\) norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the \(l_{1}\) norm based solving algorithm, \(l_{2}\) norm local sparse representation classification algorithm is proposed. This algorithm uses the minimum \(l_{2}\) norm method to select the local dictionary. Then the minimum \(l_{1}\) norm is used in the dictionary to solve sparse coefficients for classify them, and the algorithm is used to verify the gesture recognition on the constructed gesture database. The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNN-SRC algorithm.

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Acknowledgements

This work was supported by Grants of National Natural Science Foundation of China (Grant Nos. 51575407, 51575338, 51575412).

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Authors and Affiliations

  1. The Key Laboratory of Metallurgical Equipment and Control of Education Ministry, Wuhan University of Science and Technology, Wuhan, 430081, China

    Yang He, Gongfa Li, Yajie Liao, Ying Sun, Jianyi Kong, Guozhang Jiang, Du Jiang, Bo Tao & Shuang Xu

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Gongfa Li, Ying Sun, Jianyi Kong & Guozhang Jiang

  3. School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK

    Honghai Liu

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  1. Yang He
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  2. Gongfa Li
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  5. Jianyi Kong
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  7. Du Jiang
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  10. Honghai Liu
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Correspondence to Gongfa Li.

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He, Y., Li, G., Liao, Y. et al. Gesture recognition based on an improved local sparse representation classification algorithm. Cluster Comput 22 (Suppl 5), 10935–10946 (2019). https://doi.org/10.1007/s10586-017-1237-1

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  • DOI: https://doi.org/10.1007/s10586-017-1237-1

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