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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
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)
Jain, V., Learned-Miller, E.G.: FDDB: A benchmark for face detection in unconstrained settings (2010)
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)
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)
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)
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)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10, 207–244 (2009)
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)
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)
Xu, Z.E., Weinberger, K.Q., Chapelle, O.: Distance metric learning for kernel machines (2012). CoRR abs/1208.3422
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, N., Han, J., Hu, J., Deng, W. (2015). Metric Learning Based False Positives Filtering for Face Detection. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-25417-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25416-6
Online ISBN: 978-3-319-25417-3
eBook Packages: Computer ScienceComputer Science (R0)