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Metric Learning Based False Positives Filtering for Face Detection

  • Nanhai Zhang
  • Jiajie Han
  • Jiani Hu
  • Weihong Deng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9428)

Abstract

Face detection in the wild is a challenging task within the field of computer vision. Many face detectors fail to distinguish face images and non-face images because intra-class variations surpass inter-class variations. To overcome it, we propose a metric learning based false positives filtering for face detection. With 8 average faces as standard face, we apply metric learning to seek a linear transformation to reduce the distance between face images and standard faces while enlarge the distance between non-face images and standard faces. To solve our defining objective function for metric learning, we adopt a batch-stochastic gradient descent scheme, with which we can get stable solution fast. The results on FDDB and our self-collected dataset show a good performance of our method for improving Viola-Jones face detectors.

Keywords

Metric learning False positives filtering Batch-stochastic 

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References

  1. 1.
    Li, J., Wang, T., Zhang, Y.: Face detection using SURF cascade. In: IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, November 6–13, 2011, pp. 2183–2190. IEEE, Barcelona (2011)Google Scholar
  2. 2.
    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
  3. 3.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16–21, 2012, pp. 2879–2886. IEEE Computer Society, Providence (2012)Google Scholar
  4. 4.
    Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), with CD-ROM, December 8–14, 2001, pp. 511–518. IEEE Computer Society, Kauai (2001)Google Scholar
  5. 5.
    Jain, V., Learned-Miller, E.G.: FDDB: A benchmark for face detection in unconstrained settings (2010)Google Scholar
  6. 6.
    Chen, D., Ren, S., Wei, Y., Cao, X., Sun, J.: Joint cascade face detection and alignment. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 109–122. Springer, Heidelberg (2014) Google Scholar
  7. 7.
    Shen, X., Lin, Z., Brandt, J., Wu, Y.: Detecting and aligning faces by image retrieval. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23–28, 2013, pp. 3460–3467. IEEE, Portland (2013)Google Scholar
  8. 8.
    Li, H., Lin, Z., Brandt, J., Shen, X., Hua, G.: Efficient boosted exemplar-based face detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, June 23–28, 2014, pp. 1843–1850. IEEE, Columbus (2014)Google Scholar
  9. 9.
    Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.J.: Distance metric learning with application to clustering with side-information. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15 [Neural Information Processing Systems, NIPS 2002, December 9–14, 2002, Vancouver, British Columbia, Canada], pp. 505–512. MIT Press (2002)Google Scholar
  10. 10.
    Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10, 207–244 (2009)zbMATHGoogle Scholar
  11. 11.
    Pardowitz, M., Zöllner, R., Dillmann, R.: Incremental learning of task sequences with information-theoretic metrics. In: Christensen, H.I. (ed.) First European Robotics Symposium 2006, EUROS 2006, Palermo, Italy. Springer Tracts in Advanced Robotics, vol. 22, pp. 51–63. Springer (2006)Google Scholar
  12. 12.
    Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. Journal of Machine Learning Research 11, 1109–1135 (2010)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Xu, Z.E., Weinberger, K.Q., Chapelle, O.: Distance metric learning for kernel machines (2012). CoRR abs/1208.3422Google Scholar
  14. 14.
    Hu, J., Lu, J., Tan, Y.: Discriminative deep metric learning for face verification in the wild. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, June 23–28, 2014, pp. 1875–1882. IEEE, Columbus (2014)Google Scholar
  15. 15.
    Mignon, A., Jurie, F.: PCCA: A new approach for distance learning from sparse pairwise constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16–21, 2012, pp. 2666–2672. IEEE Computer Society, Providence (2012)Google Scholar
  16. 16.
    Xiong, X., la Torre, F.D.: Supervised descent method and its applications to face alignment. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23–28, 2013, pp. 532–539. IEEE, Portland (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nanhai Zhang
    • 1
  • Jiajie Han
    • 1
  • Jiani Hu
    • 1
  • Weihong Deng
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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