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Gender classification using 3D statistical models

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Abstract

In this paper, an effective gender classification based on 3D face model is proposed based on 3D principal components analysis (3D Eigenmodels) and 3D independent components analysis (3D ICmodels). In our work, the 3D face model is represented by 3D landmarks. The proposed gender classification method consists of three steps: 1) Align the 3D models to get 3D aligned shapes; 2) Perform 3D PCA/ICA transformation on the aligned 3D shapes; 3) Do gender classification on the 3D Eigenmodels/ICmodels features using SVM. The experimental results on BU_3DFE database demonstrate that the proposed method can achieve good performance.

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Acknowledgments

This project is partly supported by NSF of China (61375001, 31200747), the Natural Science Foundation of Jiangsu Province (No.BK20140638, BK20150470, BK2012437), the Fundamental Research Funds for the Central Universities (2242015 K40037), and the Open Project Program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No. CDLS-2014-04).

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Wankou Yang & Changyin Sun

  2. Key Lab of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, China

    Wankou Yang & Changyin Sun

  3. Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing, 210096, China

    Wankou Yang & Wenming Zheng

  4. Face Aging Group, Department of Computer Science, UNC Wilmington, Wilmington, NC, USA

    Karl Ricanek

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  1. Wankou Yang
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  2. Changyin Sun
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  3. Wenming Zheng
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  4. Karl Ricanek
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Correspondence to Wankou Yang.

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Yang, W., Sun, C., Zheng, W. et al. Gender classification using 3D statistical models. Multimed Tools Appl 76, 4491–4503 (2017). https://doi.org/10.1007/s11042-016-3446-7

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  • DOI: https://doi.org/10.1007/s11042-016-3446-7

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