Skip to main content
SpringerLink
Log in
Book cover

Proceedings of ELM-2016 pp 217–228Cite as

Facial Landmark Detection via ELM Feature Selection and Improved SDM

  • Conference paper
  • First Online:

  • 891 Accesses

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 9))

Abstract

Model initialization and feature extraction are crucial in supervised landmark detection. Mismatching caused by detector error and discrepant initialization is very common in these existing methods. To solve this problem, we have proposed a new method based on ELM feature selection and Improved Supervised Descent Method (ELMFS-iSDM), which also includes an automatic initialization model, for the robust facial landmark localization. In our new method, firstly, a fast detection will be processed to locate the eyes and mouth, and the initialization model will adapt to the real location according to fast facial points detection. Secondly, ELM based feature selection is adopted on our Improved Supervised Descent Method model to achieve a better performance. For each task, multiple features will be jointly learned by ELM feature selection and their weights will be calculated during training process. Experiments on four benchmark databases show that our method achieves state-of-the-art performance.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Gupta, O.P., et al.: Robust facial landmark detection using a mixture of synthetic and real images with dynamic weighting: a survey. Sci. Eng. Tech. 25 (2016)

    Google Scholar 

  2. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2879–2886 (2012)

    Google Scholar 

  3. Matthews, I., Baker, S.: Active appearance models revisited. Int. J. Comput. Vis. 60(2), 135–164 (2004)

    Article  Google Scholar 

  4. Blake, A., Isard M.: Active shape models. In: Active Contours, pp. 25–37. Springer (1998)

    Google Scholar 

  5. Cristinacce, D., Cootes, T.F.: Feature detection and tracking with constrained local models. In: BMVC, vol.1, p. 3 (2006)

    Google Scholar 

  6. Xiong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 532–539 (2013)

    Google Scholar 

  7. Dollár, P., Welinder, P., Perona, P.: Cascaded pose regression. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1078–1085 (2012)

    Google Scholar 

  8. Ren, S., Cao, X., Wei, Y., Sun, J.: Face alignment at 3000 fps via regressing local binary features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1685–1692 (2014)

    Google Scholar 

  9. Worth, C.L., Preissner, R., Blundell, T.L.: Sdma server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res. 39(suppl 2), W215–W222 (2011)

    Article  Google Scholar 

  10. Huang, G.-B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2(2), 107–122 (2011)

    Article  Google Scholar 

  11. Cao, J., Lin, Z.: Extreme learning machines on high dimensional and large data applications: a survey. Math. Probl. Eng. 501, 103796 (2015)

    Google Scholar 

  12. Cao, J., Zhang, K., Luo, M., Yin, C., Lai, X.: Extreme learning machine and adaptive sparse representation for image classification. Neural Netw. 81, 91–102 (2016)

    Article  Google Scholar 

  13. Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Sys. Man Cybern. Part B: Cybern. 42(2), 513–529 (2012)

    Article  Google Scholar 

  14. Zong, W., Huang, G.-B.: Face recognition based on extreme learning machine. Neurocomputing 74(16), 2541–2551 (2011)

    Article  Google Scholar 

  15. Zong, W., Zhou, H., Huang, G.-B., Lin, Z.: Face recognition based on kernelized extreme learning machine. In: Autonomous and Intelligent Systems. Lecture Notes in Computer Science, vol. 6752, pp. 263–272 (2011)

    Google Scholar 

  16. Long, X., Lu, H., Peng, Y., Li, W.: Graph regularized discriminative non-negative matrix factorization for face recognition. Multimed. Tools Appl. 72(3), 2679–2699 (2014)

    Article  Google Scholar 

  17. Cao, J., Chen, T., Fan, J.: Landmark recognition with compact BoW histogram and ensemble ELM. Multimed. Tools Appl. 75(5), 2839–2857 (2016)

    Article  Google Scholar 

  18. Cao, J., Zhao, Y., Lai, X., Ong, M.E.H., Yin, C., Koh, Z.X., Liu, N.: Landmark recognition with sparse representation classification and extreme learning machine. J. Frankl. Inst. 352(10), 4528–4545 (2015)

    Article  MathSciNet  Google Scholar 

  19. Roul, R.K., Gugnani, S., Kalpeshbhai, S.M.: Clustering based feature selection using extreme learning machines for text classification. In: 2015 Annual IEEE India Conference (INDICON) pp. 1–6 (2015)

    Google Scholar 

  20. Zhai, M.-Y., Yu, R.-H., Zhang, S.-F., Zhai, J.-H.: Feature selection based on extreme learning machine. In: 2012 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 157–162 (2012)

    Google Scholar 

  21. Viola, P., Jones, M.J.: Robust real-time face detection. Inter. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  22. Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 572–581 (2004)

    Article  Google Scholar 

  23. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Inter. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  24. Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2930–2940 (2013)

    Article  Google Scholar 

  25. Le, V., Brandt, J., Lin, Z., Bourdev, L., Huang, T.S.: Interactive facial feature localization. In: European Conference on Computer Vision, pp. 679–692. Springer (2012)

    Google Scholar 

  26. Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: the first facial landmark localization challenge. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 397–403 (2013)

    Google Scholar 

  27. Asthana, A., Zafeiriou, S., Cheng, S. Pantic, M.: Robust discriminative response map fitting with constrained local models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3444–3451 (2013)

    Google Scholar 

  28. Zhang, J., Shan, S., Kan, M., Chen, X.: Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment. In: European Conference on Computer Vision, pp. 1–16. Springer (2014)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 51505004, 61403024, 61471032), the National Key Basic Research Program of China (2012CB316304) and the Beijing Natural Science Foundation (No.4163075, 4162048).

Author information

Authors and Affiliations

  1. College of Mechanical and Material Engineering, North China University of Technology, Beijing, 100144, People’s Republic of China

    Peng Bian

  2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Yi Jin

  3. Beijing Key Lab of Traffic Data Analysis and Mining, Beijing, 100044, People’s Republic of China

    Yi Jin

  4. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, 310018, China

    Jiuwen Cao

Authors
  1. Peng Bian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiuwen Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Jin .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  3. Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

  4. Department of Information and Computer Science, School of Science, Aalto University, Aalto, Finland

    Yoan Miche

  5. Department of Computer and Information Science, University of Macau, Macau, China

    Chi Man Vong

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Bian, P., Jin, Y., Cao, J. (2018). Facial Landmark Detection via ELM Feature Selection and Improved SDM. In: Cao, J., Cambria, E., Lendasse, A., Miche, Y., Vong, C. (eds) Proceedings of ELM-2016. Proceedings in Adaptation, Learning and Optimization, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-57421-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57421-9_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57420-2

  • Online ISBN: 978-3-319-57421-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation