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Fusion of Global and Local Gaussian-Hermite Moments for Face Recognition

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Image and Graphics Technologies and Applications (IGTA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1043))

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Abstract

In automatically recognizing human faces, it is an important problem how to extract the effective features from the corrupted face. This paper propose a new face recognition algorithm based on fusion of global and local Gaussian-Hermite moments (GHMs). Firstly, in order to solve the interference of noise on features, we use the GHMs of face image as facial feature. Second, we construct the face image spatial pyramid to extract the global and local features of the face, and then we compute scatter-ratio to seclect highly discriminative feature. Lastly we use sparse representation classifier to improve the robust of algorithm. Experiments on ORL, FERET and Yale A face databases reveal that the accuracy of proposed algorithm is better than traditional algorithm, especially when the face images are corrupted by salt&pepper noise.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 41674141 and No. 41204074.

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Authors and Affiliations

  1. School of Science, Southwest Petroleum University, Chengdu, China

    Guojie Song, Dan He, Puchun Chen, Jidong Tian, Bin Zhou & Li Luo

Authors
  1. Guojie Song
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  2. Dan He
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  3. Puchun Chen
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  4. Jidong Tian
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  5. Bin Zhou
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  6. Li Luo
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Corresponding author

Correspondence to Guojie Song .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. University of Chinese Academy of Science, Beijing, China

    Qingmin Huang

  3. Institute of Computer Science and Technology, Peking University, Beijing, China

    Yuxin Peng

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Song, G., He, D., Chen, P., Tian, J., Zhou, B., Luo, L. (2019). Fusion of Global and Local Gaussian-Hermite Moments for Face Recognition. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_17

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  • DOI: https://doi.org/10.1007/978-981-13-9917-6_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9916-9

  • Online ISBN: 978-981-13-9917-6

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