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Human facial expression analysis based on image granule LPP

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Abstract

This paper proposes a synthetic technique for human facial expression analysis based on locality preserving projections (LPP) and granular computing. The proposed method decreases the computational complexity of LPP and preserves the performance of LPP algorithm. In image processing, an image can be divided into various sizes of blocks, which are defined as image granules in this paper. The LPP algorithm is implemented to images with various image granules. By this strategy, the image dimension reduces quickly, which decreases the computational complexity. The experiments on three face databases are presented to show the performance of image granule LPP. Distribution of the facial pose and expression is shown and the computational complexities with different image granules are compared. Meanwhile, the order-preserving property of images is investigated by tracking the sequence of designated images. And the loss of image information is analyzed by image roughness, entropy and histogram. Furthermore, the parameter setting in LPP is discussed because of its non-ignorable affect on the experiment results. Finally, the method is applied to facial expression recognition.

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Acknowledgments

The authors would like to thank the anonymous reviewers for suggesting many ways to improve the paper. The work is partially supported by the National Natural Science Foundation of China (No. 61170121).

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Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, China

    Xiuxiu Xu, Jiuzhen Liang, Sisi Lv & Qin Wu

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  1. Xiuxiu Xu
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  2. Jiuzhen Liang
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  4. Qin Wu
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Correspondence to Jiuzhen Liang.

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Xu, X., Liang, J., Lv, S. et al. Human facial expression analysis based on image granule LPP. Int. J. Mach. Learn. & Cyber. 5, 907–921 (2014). https://doi.org/10.1007/s13042-014-0228-2

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  • DOI: https://doi.org/10.1007/s13042-014-0228-2

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