Advertisement

Joint Cascade Face Detection and Alignment

  • Dong Chen
  • Shaoqing Ren
  • Yichen Wei
  • Xudong Cao
  • Jian Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

We present a new state-of-the-art approach for face detection. The key idea is to combine face alignment with detection, observing that aligned face shapes provide better features for face classification. To make this combination more effective, our approach learns the two tasks jointly in the same cascade framework, by exploiting recent advances in face alignment. Such joint learning greatly enhances the capability of cascade detection and still retains its realtime performance. Extensive experiments show that our approach achieves the best accuracy on challenging datasets, where all existing solutions are either inaccurate or too slow.

Keywords

Face Detection Face Shape Sift Descriptor Split Test Facial Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bourdev, L.D., Brandt, J.: Robust Object Detection via Soft Cascade. In: Computer Vision and Pattern Recognition, vol. 2, pp. 236–243 (2005)Google Scholar
  2. 2.
    Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Brubaker, S.C., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M.: On the Design of Cascades of Boosted Ensembles for Face Detection. IJCV 77, 65–86 (2008)CrossRefGoogle Scholar
  4. 4.
    Cao, X., Wei, Y., Wen, F., Sun, J.: Face Alignment by Explicit Shape Regression. In: Computer Vision and Pattern Recognition (2012)Google Scholar
  5. 5.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1627–1645 (2010)CrossRefGoogle Scholar
  6. 6.
    Gall, J., Lempitsky, V.S.: Class-specific Hough forests for object detection. In: Computer Vision and Pattern Recognition, pp. 1022–1029 (2009)Google Scholar
  7. 7.
    Huang, C., Ai, H., Li, Y., Lao, S.: High-Performance Rotation Invariant Multiview Face Detection. IEEE Transactions on PAMI 29, 671–686 (2007)CrossRefGoogle Scholar
  8. 8.
    Jain, V., Learned-Miller, E.: Online domain adaptation of a pre-trained cascade of classifiers. In: CVPR (2011)Google Scholar
  9. 9.
    Jain, V., Learned-Miller, E.: Fddb: A benchmark for face detection in unconstrained settings. Tech. Rep. UM-CS-2010-009, University of Massachusetts, Amherst (2010)Google Scholar
  10. 10.
    Jones, M.J., Viola, P.: Fast Multi-view Face Detection. In: CVPR (2003)Google Scholar
  11. 11.
    Kalal, Z., Matas, J., Mikolajczyk, K.: Weighted Sampling for Large-Scale Boosting. In: British Machine Vision Conference (2008)Google Scholar
  12. 12.
    Koestinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Robust face detection by simple means. In: DAGM 2012 CVAW WorkshopGoogle Scholar
  13. 13.
    Li, H., Lin, Z., Brandt, J., Shen, X., Hua, G.: Efficient boosted exemplar-based face detection. In: CVPR (2014)Google Scholar
  14. 14.
    Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation. In: ICCV (2013)Google Scholar
  15. 15.
    Li, J., Zhang, Y.: Learning surf cascade for fast and accurate object detection. In: CVPR (2013)Google Scholar
  16. 16.
    Li, J., Wang, T., Zhang, Y.: Face detection using SURF cascade. In: International Conference on Computer Vision (2011)Google Scholar
  17. 17.
    Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.: Statistical Learning of Multi-view Face Detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Dollar, P., Welinder, P., Perona, P.: Cascaded pose regression. In: CVPR (2010)Google Scholar
  20. 20.
    Pham, M.T., Gao, Y., Hoang, V.D.D., Cham, T.J.: Fast polygonal integration and its application in extending haar-like features to improve object detection. In: Computer Vision and Pattern Recognition, pp. 942–949 (2010)Google Scholar
  21. 21.
    Ren, S., Cao, X., Wei, Y., Sun, J.: Face Alignment at 3000 FPS via Regressing Local Binary Features. In: Computer Vision and Pattern Recognition (2014)Google Scholar
  22. 22.
    Schneiderman, H., Kanade, T.: Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition. In: CVPR, pp. 45–51 (1998)Google Scholar
  23. 23.
    Segui, S., Drozdzal, M., Radeva, P., Vitri, J.: An integrated approach to contextual face detection. In: ICPRAM (2012)Google Scholar
  24. 24.
    Shen, X., Lin, Z., Brandt, J., Wu, Y.: Detecting and Aligning Faces by Image Retrieval. In: Computer Vision and Pattern Recognition (2013)Google Scholar
  25. 25.
    Venkatesh, B.S., Marcel, S.: Fast bounding box estimation based face detection. In: ECCV Workshop on Face Detection (2010)Google Scholar
  26. 26.
    Viola, P.A., Jones, M.J.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  27. 27.
    Wu, B., Ai, H., Huang, C., Lao, S.: Fast Rotation Invariant Multi-View Face Detection Based on Real Adaboost. In: ICAFGR, pp. 79–84 (2004)Google Scholar
  28. 28.
    Xiong, X., DelaTorre, F.: Supervised Descent Method and its Applications to Face Alignment. In: Computer Vision and Pattern Recognition (2013)Google Scholar
  29. 29.
    Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Computer Vision and Pattern Recognition (2013)Google Scholar
  30. 30.
    Yan, J., Lei, Z., Wen, L., Li, S.Z.: The fastest deformable part model for object detection. In: CVPR (2014)Google Scholar
  31. 31.
    Zhang, C., Zhang, Z.: A Survey of Recent Advances in Face Detection (2010)Google Scholar
  32. 32.
    Zhu, X., Ramanan, D.: Face detection, pose estimation and landmark localization in the wild. In: Computer Vision and Pattern Recognition (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dong Chen
    • 1
  • Shaoqing Ren
    • 1
  • Yichen Wei
    • 2
  • Xudong Cao
    • 2
  • Jian Sun
    • 2
  1. 1.University of Science and Technology of ChinaChina
  2. 2.Microsoft ResearchUSA

Personalised recommendations