Skip to main content
SpringerLink
Log in

Improving Deep Unconstrained Facial Recognition by Data Augmentation

  • Chapter
  • First Online:
Implementations and Applications of Machine Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 782))

  • 943 Accesses

Abstract

Facial recognition technology has emerged as an attractive solution for many of today’s needs in identification and identity verification. In recent years, the use of deep learning techniques and convolutional neural networks in particular has led to high-performance systems with near-human recognition capabilities. In general, these models are trained and evaluated on image datasets that do not sufficiently consider the lighting conditions of a real environment. However, in many practical applications the lighting is uncontrolled, which may seriously affect the performance of these systems. In this chapter, we propose a data augmentation method to achieve a model that is robust to variations in brightness. The training dataset is augmented by generating 3D faces from 2D images in the original dataset, followed by a Lambertian reflectance lighting variation that simulates the lighting variations that occur in real environments. The approach is evaluated on the YaleB and ORL datasets, with respective accuracy gains of 17.77% and 9%, compared to the model trained without data augmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., TensorFlow: a system for large-scale machine learning, in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16) (2016), pp. 265–283

    Google Scholar 

  2. M. Attene, M. Campen, L. Kobbelt, Polygon mesh repairing: an application perspective. ACM Comput. Surv. (CSUR) 45(2), 15 (2013)

    Google Scholar 

  3. S. Balakrishnama, A. Ganapathiraju, Linear discriminant analysis-a brief tutorial. Inst. Signal Inf. Process. 18, 1–8 (1998)

    Google Scholar 

  4. R. Basri, D.W. Jacobs, Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)

    Article  Google Scholar 

  5. V. Blanz, T. Vetter et al., A morphable model for the synthesis of 3d faces, in SIGGRAPH ’99 (1999), pp. 187–194

    Google Scholar 

  6. P. Borodin, G. Zachmann, R. Klein, Consistent normal orientation for polygonal meshes, in Proceedings Computer Graphics International, 2004 (IEEE, Piscataway, 2004), pp. 18–25

    Google Scholar 

  7. A.M. Bronstein, M.M. Bronstein, R. Kimmel, Expression-invariant 3d face recognition, in International Conference on Audio- and video-based Biometric Person Authentication (Springer, Berlin, 2003), pp. 62–70

    MATH  Google Scholar 

  8. T. Carneiro, R.V.M. Da Nóbrega, T. Nepomuceno, G.-B. Bian, V.H.C. De Albuquerque, P.P. Reboucas Filho, Performance analysis of Google Colaboratory as a tool for accelerating deep learning applications. IEEE Access 6, 61677–61685 (2018)

    Article  Google Scholar 

  9. E. Cengil, A. Çinar, Comparison of Hog (histogram of oriented gradients) and Haar Cascade algorithms with a convolutional neural network based face detection approach. Comput. Sci. 3(5), 244–255 (2017)

    Google Scholar 

  10. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  11. Q.-K. Do, A. Allauzen, F. Yvon, Modèles de langue neuronaux: une comparaison de plusieurs stratégies d’apprentissage, in Actes de la 21e conférence sur le traitement automatique des langues naturelles (TALN) (2014), pp. 256–267

    Google Scholar 

  12. Y. Feng, F. Wu, X. Shao, Y. Wang, X. Zhou, Joint 3d face reconstruction and dense alignment with position map regression network, in Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp. 534–551

    Google Scholar 

  13. A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 643–660 (2001)

    Article  Google Scholar 

  14. G. Ghiasi, T.-Y. Lin, Q.V. Le, DropBlock: a regularization method for convolutional networks, in Advances in Neural Information Processing Systems (2018), pp. 10727–10737

    Google Scholar 

  15. X. Guo, J. Xiao, Y. Wang, A survey on algorithms of hole filling in 3d surface reconstruction. Vis. Comput. 34(1), 93–103 (2018)

    Article  Google Scholar 

  16. G.B. Huang, M. Mattar, T. Berg, E. Learned-Miller, Labeled faces in the wild: a database for studying face recognition in unconstrained environments, in Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (2008)

    Google Scholar 

  17. S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). Preprint. arXiv: 1502.03167

    Google Scholar 

  18. S. Jahanbin, H. Choi, Y. Liu, A.C. Bovik, Three dimensional face recognition using iso-geodesic and iso-depth curves, in 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems (IEEE, Piscataway, 2008), pp. 1–6

    Google Scholar 

  19. D. Jiang, Y. Hu, S. Yan, L. Zhang, H. Zhang, W. Gao, Efficient 3d reconstruction for face recognition. Pattern Recogn. 38(6), 787–798 (2005)

    Article  Google Scholar 

  20. I. Jolliffe, Principal Component Analysis (Springer, New York, 2011)

    MATH  Google Scholar 

  21. D.E. King, Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10(Jul), 1755–1758 (2009)

    Google Scholar 

  22. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097–1105

    Google Scholar 

  23. J. Lawrence, J. Malmsten, A. Rybka, D.A. Sabol, K. Triplin, Comparing TensorFlow deep learning performance using CPUs, GPUs, local PCs and cloud, in Proceedings of Student-Faculty Research Day, CSIS, Pace University (2017)

    Google Scholar 

  24. E. Learned-Miller, G.B. Huang, A. RoyChowdhury, H. Li, G. Hua, Labeled faces in the wild: a survey, in Advances in Face Detection and Facial Image Analysis (Springer, Cham, 2016), pp. 189–248

    Google Scholar 

  25. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436 (2015)

    Google Scholar 

  26. S.P. Lim, H. Haron, Surface reconstruction techniques: a review. Artif. Intell. Rev. 42(1), 59–78 (2014)

    Article  Google Scholar 

  27. X. Liu, M. Kan, W. Wu, S. Shan, X. Chen, VIPLFaceNet: an open source deep face recognition SDK. Front. Comput. Sci. 11(2), 208–218 (2017)

    Article  Google Scholar 

  28. S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)

    Article  Google Scholar 

  29. O.M. Parkhi, A. Vedaldi, A. Zisserman et al., Deep face recognition, in BMVC, vol. 1 (2015), p. 6

    Google Scholar 

  30. P. Paysan, R. Knothe, B. Amberg, S. Romdhani, T. Vetter, A 3d face model for pose and illumination invariant face recognition, in 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (IEEE, Piscataway, 2009), pp. 296–301

    Google Scholar 

  31. J. Roth, Y. Tong, X. Liu, Unconstrained 3d face reconstruction, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 2606–2615

    Google Scholar 

  32. S.R. Safavian, D. Landgrebe, A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)

    Article  MathSciNet  Google Scholar 

  33. F.S. Samaria, Face recognition using hidden Markov models. PhD thesis, University of Cambridge, Cambridge, UK, 1994

    Google Scholar 

  34. F. Schroff, D. Kalenichenko, J. Philbin, FaceNet: a unified embedding for face recognition and clustering, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 815–823

    Google Scholar 

  35. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition (2015). Preprint. arXiv: 1409.1556v6

    Google Scholar 

  36. L. Sixt, B. Wild, T. Landgraf, RenderGAN: generating realistic labeled data. Front. Robot. AI 5, 66 (2018)

    Article  Google Scholar 

  37. Y. Sun, Y. Chen, X. Wang, X. Tang, Deep learning face representation by joint identification-verification, in Advances in Neural Information Processing Systems (2014), pp. 1988–1996

    Google Scholar 

  38. Y. Sun, X. Wang, X. Tang, Deep learning face representation from predicting 10,000 classes, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1891–1898

    Google Scholar 

  39. Y. Sun, D. Liang, X. Wang, X. Tang, Deepid3: face recognition with very deep neural networks (2015). Preprint. arXiv: 1502.00873

    Google Scholar 

  40. Y. Sun, X. Wang, X. Tang, Deeply learned face representations are sparse, selective, and robust, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 2892–2900

    Google Scholar 

  41. Y. Taigman, M. Yang, M. Ranzato, L. Wolf, DeepFace: closing the gap to human-level performance in face verification, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1701–1708

    Google Scholar 

  42. J. Wang, L. Yin, X. Wei, Y. Sun, 3d facial expression recognition based on primitive surface feature distribution, in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2 (IEEE, Piscataway, 2006), pp. 1399–1406

    Google Scholar 

  43. K.Q. Weinberger, J. Blitzer, L.K. Saul, Distance metric learning for large margin nearest neighbor classification, in Advances in Neural Information Processing Systems (2006), pp. 1473–1480

    Google Scholar 

  44. M.D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in European Conference on Computer Vision (Springer, Cham, 2014), pp. 818–833

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Dschang, Dschang, Cameroon

    Arnauld Fountsop Nzegha & Clementin Djameni Tayou

  2. Department of Computer Engineering, Institute of Technology, University of Ngaoundere, Ngaoundere, Cameroon

    Jean Louis Ebongue Fendji

  3. Department of Science and Mathematics, Texas A&M University-Central Texas, Killeen, TX, USA

    Christopher Thron

Authors
  1. Arnauld Fountsop Nzegha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean Louis Ebongue Fendji
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher Thron
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Clementin Djameni Tayou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Computer Studies, International University of Africa (IUA), Khartoum, Sudan

    Saad Subair

  2. Department of Science and Mathematics, Texas A&M University-Central Texas, Killeen, TX, USA

    Christopher Thron

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nzegha, A.F., Fendji, J.L.E., Thron, C., Tayou, C.D. (2020). Improving Deep Unconstrained Facial Recognition by Data Augmentation. In: Subair, S., Thron, C. (eds) Implementations and Applications of Machine Learning. Studies in Computational Intelligence, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-030-37830-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37830-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37829-5

  • Online ISBN: 978-3-030-37830-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation