Skip to main content
SpringerLink
Log in
Book cover

International Conference on Multimedia Modeling

MMM 2019: MultiMedia Modeling pp 289–299Cite as

Incremental Training for Face Recognition

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11295))

Abstract

Many applications require the identification of persons in video. However, the set of persons of interest is not always known in advance, e.g., in applications for media production and archiving. Additional training samples may be added during the analysis, or groups of faces of one person may need to be identified retrospectively. In order to avoid re-running the face recognition, we propose an approach that supports fast incremental training based on a state of the art face detection and recognition pipeline using CNNs and an online random forest as a classifier. We also describe an algorithm to use the incremental training approach to automatically train classifiers for unknown persons, including safeguards to avoid noise in the training data. We show that the approach reaches state of the art performance on two datasets when using all training samples, but performs better with few or even only one training sample.

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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    https://github.com/davidsandberg/facenet/tree/master/src/align.

  2. 2.

    https://www.tensorflow.org.

  3. 3.

    https://github.com/davidsandberg/facenet.

  4. 4.

    https://www.nist.gov/itl/iad/image-group/color-feret-database.

References

  1. Amos, B., Ludwiczuk, B., Satyanarayanan, M., et al.: OpenFace: a general-purpose face recognition library with mobile applications. Technical Report CMU-CS-16-118, CMU School of Computer Science (2016)

    Google Scholar 

  2. Breimann, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  3. Chen, S., Liu, Y., Gao, X., Han, B.: MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. In: Chinese Conference on Biometric Recognition (2018)

    Google Scholar 

  4. Choi, K., Toh, K.-A., Byun, H.: Incremental face recognition for large-scale social network services. Pattern Recognit. 45(8), 2868–2883 (2012)

    Article  Google Scholar 

  5. Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends Comput. Graph. Vis. 7, 81–227 (2012)

    Article  Google Scholar 

  6. Deng, J., Guo, J., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. CoRR, abs/1801.07698 (2018)

    Google Scholar 

  7. Farfade, S.S., Saberian, M.J., Li, L.-J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 643–650. ACM (2015)

    Google Scholar 

  8. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst.Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  Google Scholar 

  9. Freund, Y., Schapire, R.E.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771–780 (1999). English translation

    Google Scholar 

  10. Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: challenge of recognizing one million celebrities in the real world. Electron. Imaging 2016(11), 1–6 (2016)

    Article  Google Scholar 

  11. Jiang, H., Learned-Miller, E.: Face detection with the faster R-CNN. In: 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, pp. 650–657. IEEE (2017)

    Google Scholar 

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

    Google Scholar 

  13. Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Kawulok, M., Celebi, M.E., Smolka, B. (eds.) Advances in Face Detection and Facial Image Analysis, pp. 189–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25958-1_8

    Chapter  Google Scholar 

  14. Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)

    Google Scholar 

  15. Mathias, M., Benenson, R., Pedersoli, M., Van Gool, L.: Face detection without bells and whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 720–735. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_47

    Chapter  Google Scholar 

  16. Oza, N.C., Russell, S.: Online bagging and boosting. In: Eighth International Workshop on Artificial Intelligence and Statistics, pp. 105–112 (2001)

    Google Scholar 

  17. Ozawa, S., Toh, S.L., Abe, S., Pang, S., Kasabov, N.: Incremental learning of feature space and classifier for face recognition. Neural Netw. 18(5–6), 575–584 (2005)

    Article  Google Scholar 

  18. Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2017)

    Google Scholar 

  19. Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–38 (1998)

    Article  Google Scholar 

  20. Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pages 1393–1400. IEEE (2009)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  23. Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR, abs/1602.07261 (2016)

    Google Scholar 

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

    Google Scholar 

  25. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (2013)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  27. Wong, Y.W., Seng, K.P., Ang, L.M.: Radial basis function neural network with incremental learning for face recognition. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(4), 940–949 (2011)

    Article  Google Scholar 

  28. Yan, J., Lei, Z., Wen, L., Li, S.Z.: The fastest deformable part model for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2497–2504 (2014)

    Google Scholar 

  29. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  30. Zhu, C., Zheng, Y., Luu, K., Savvides, M.: CMS-RCNN: contextual multi-scale region-based CNN for unconstrained face detection. arXiv preprint arXiv:1606.05413 (2016)

  31. Zhu, Z., Luo, P., Wang, X., Tang, X.: Recover canonical-view faces in the wild with deep neural networks. arXiv preprint arXiv:1404.3543 (2014)

Download references

Acknowledgments

The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no 732461, ReCAP (“Real-time Content Analysis and Processing”, http://recap-project.com), and 761802, MARCONI (“Multimedia and Augmented Radio Creation: Online, iNteractive, Individual”, https://www.projectmarconi.eu).

Author information

Authors and Affiliations

  1. JOANNEUM RESEARCH Forschungsgesellschaft mbH, DIGITAL – Institute for Information and Communication Technologies, Steyrergasse 17, 8010, Graz, Austria

    Martin Winter & Werner Bailer

Authors
  1. Martin Winter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Werner Bailer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Werner Bailer .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  6. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Winter, M., Bailer, W. (2019). Incremental Training for Face Recognition. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05710-7_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05709-1

  • Online ISBN: 978-3-030-05710-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation