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Expansion of 3D face sample set based on genetic algorithm

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Abstract

3D face database is an important data platform for model training and algorithm design. As these works are mainly based on statistical learning, the coverage of training set has a great impact on algorithm performance. However it is a tedious process to obtain a 3D face sample. So the capacities of current 3D face databases are relatively insufficient. To solve this problem, we present a framework to augment existing 3D databases based on Genetic Algorithm. First the prototypical face samples are divided into patches. Then the new face samples are generated by assembling randomly selected patches. Under the guidance of the genetic algorithm, we can perform a number of the generating works at a time. The experiment results show that the proposed method has good performance on face data expansion.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (N0.60973057, 60825203, 61133003, 61171169), Guangdong Provincial Science and Technology project (2010A090100019).

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Authors and Affiliations

  1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Yun Ge, Bao-cai Yin, Yan-feng Sun & Guo-dong Jing

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  1. Yun Ge
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  2. Bao-cai Yin
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  3. Yan-feng Sun
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  4. Guo-dong Jing
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Correspondence to Yan-feng Sun.

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Ge, Y., Yin, Bc., Sun, Yf. et al. Expansion of 3D face sample set based on genetic algorithm. Multimed Tools Appl 70, 781–797 (2014). https://doi.org/10.1007/s11042-012-1102-4

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  • DOI: https://doi.org/10.1007/s11042-012-1102-4

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