Multi-pose Face Recognition Based on Contour Symmetric Constraint-Generative Adversarial Network

  • Ning Ouyang
  • Liyuan Liu
  • Leping LinEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


In order to address the impact of large-angle posture changes on face recognition performance, we propose a contour symmetric constraint-generative adversarial network (CSC-GAN) for the multi-pose face recognition. The method employs the convolutional network as the generator for face pose recovery, which introduces the global information of the constrained pose recovery of positive face contour histogram. Meanwhile, the original positive face is used as the discriminator, and the symmetric loss function is added to optimize the learning ability of the network. The positive face with gesture recovery is obtained by striking the balance between training of the generator and discriminator. Then we employed the nearest neighbor classifier to identify. The experimental results show that CSC-GAN obtained good posture reconstruction texture information on the multi-pose face reconstruction. Compared with the traditional deep learning method and 3D method, it also achieves higher recognition rate.


Generative adversarial network Pose recovery Face contour Symmetric loss 



This work is partially supported by the following foundations: the National Natural Science Foundation of China (61661017); the China Postdoctoral Science Fund Project (2016M602923XB); the Natural Science Foundation of Guangxi province (2017GXNSFBA198212, 2016GXNSFAA38014); the Key Laboratory Fund of Cognitive Radio and Information Processing (CRKL160104, CRKL150103, 2011KF11); Innovation Project of GUET Graduate Education (2016YJCXB02); the Scientific and Technological Innovation Ability and Condition Construction Plans of Guangxi (159802521); the Scientific and Technological Bureau of Guilin (20150103-6).


  1. 1.
    Zhu Z, Luo P, Wang X, et al. Deep learning identity-preserving face space. In: Proceedings of the IEEE international conference on computer vision. Darling Harbour, Sydney; 2013.Google Scholar
  2. 2.
    Taigman Y, Yang M, Ranzato MA, Wolf L. Closing the gap to human-level performance in face verification. In: CVPR, Colombia; 2014.Google Scholar
  3. 3.
    Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes. In: CVPR, Colombia; 2014.Google Scholar
  4. 4.
    Taigman Y, Yang M, Ranzato MA, Wolf L. Web-scale training for face identification. arXiv:1406.5266; 2014.
  5. 5.
    Asthana A, Marks TK, Jones MJ, Tieu KH, Rohith M. Fully automatic pose-invariant face recognition via 3D pose normalization. In: ICCV, Barcelona, Spain; 2011.Google Scholar
  6. 6.
    Kan M, Shan S, Chang H, et al. Stacked progressive auto-encoders (spae) for face recognition across poses. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2014. p. 1883–90.Google Scholar
  7. 7.
    Zhu Z, Luo P, Wang X, et al. Multi-view perceptron: a deep model for learning face identity and view representations. In: Advances in Neural Information Processing Systems; 2014. p. 217–25.Google Scholar
  8. 8.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, et al. Generative adversarial nets. In: NIPS, Montreal, Canada; 2014.Google Scholar
  9. 9.
    Gross R, Matthews I, Cohn J, et al. The CMU multi-pose, illumination, and expression (multi-PIE) face database. CMU Robotics Institute. TR-07-08, Tech. Rep; 2007.Google Scholar
  10. 10.
    Zhang W, Shan S, Gao W, Chen X, Zhang H. Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: ICCV, Beijing, China; 2005.Google Scholar
  11. 11.
    Huang GB, Lee H, Learned-Miller E. Learning hierarchical representations for face verification with convolutional deep belief networks. In: CVPR, Rhode Island, America; 2012.Google Scholar
  12. 12.
    Li S, Liu X, Chai X, Zhang H, Lao S, Shan S. Morphable displacement field based image matching for face recognition across pose. In: ECCV, Florence, Italy; 2012.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Key Laboratory of Cognitive Radio and Information Processing, Ministry of EducationGuilin University of Electronic TechnologyGuilinChina
  2. 2.School of Information and CommunicationGuilin University of Electronic TechnologyGuilinChina

Personalised recommendations