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Distributed SVM face recognition based on Hadoop

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Abstract

In the work, we analyzed the face recognition method under the cloud computing platform. Based on the distributed support vector machine classification model, face recognition method was established on Hadoop platform to develop parallel computing advantage of MapReduce, thus improving recognition efficiency. With different functions in face feature recognition, conventional and depth LBP operators were integrated by weighted approach to play their respective advantages. Finally, face recognition method under cloud computing platform was conducted with example analysis by Yale B, ORL and FERET face databases, which were most widely used in face recognition field. Under the same cloud computing platform, the classifier was established by BP and RBF neural networks to compare with the research method in the work. The results showed that the SVM classifier has better face recognition effect than BP and RBF neural network classifier under cloud computing platform. The research method in the work has higher recognition accuracy than traditional method.

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Acknowledgements

The paper is subsidized by science and technology key project of Henan Province, NO. 172102210462.

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  1. School of Information Engineering, Xuchang University, Xuchang, 461000, Henan, China

    Boping Zhang

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  1. Boping Zhang
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Correspondence to Boping Zhang.

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Zhang, B. Distributed SVM face recognition based on Hadoop. Cluster Comput 22 (Suppl 1), 827–834 (2019). https://doi.org/10.1007/s10586-017-1330-5

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