Dynamic Selection of Exemplar-SVMs for Watch-list Screening through Domain Adaptation

Saman Bashbaghi, Eric Granger, Robert Sabourin, Guillaume-Alexandre Bilodeau

Abstract

Still-to-video face recognition (FR) plays an important role in video surveillance, allowing to recognize individuals of interest over a network of video cameras. Watch-list screening is a challenging video surveillance application, because faces captured during enrollment (with still camera) may differ significantly from those captured during operations (with surveillance cameras) under uncontrolled capture conditions (with variations in, e.g., pose, scale, illumination, occlusion, and blur). Moreover, the facial models used for matching are typically designed a priori with a limited number of reference stills. In this paper, a multi-classifier system is proposed that exploits domain adaptation and multiple representations of face captures. An individual-specific ensemble of exemplar-SVM (e-SVM) classifiers is designed to model the single reference still of each target individual, where different random subspaces, patches, and face descriptors are employed to generate a diverse pool of classifiers. To improve robustness of face models, e-SVMs are trained using the limited number of labeled faces in reference stills from the enrollment domain, and an abundance of unlabeled faces in calibration videos from the operational domain. Given the availability of a single reference target still, a specialized distance-based criteria is proposed based on properties of e-SVMs for dynamic selection of the most competent classifiers per probe face. The proposed approach has been compared to reference systems for still-to-video FR on videos from the COX-S2V dataset. Results indicate that ensemble of e-SVMs designed using calibration videos for domain adaptation and dynamic ensemble selection yields a high level of FR accuracy and computational efficiency.

References

  1. Ahonen, T., Rahtu, E., Ojansivu, V., and Heikkila, J. (2008). Recognition of blurred faces using local phase quantization. In ICPR, pages 1-4.
  2. Barr, J. R., Bowyer, K. W., Flynn, P. J., and Biswas, S. (2012). Face recognition from video: A review. IJPRAI, 26(05).
  3. Bashbaghi, S., Granger, E., Sabourin, R., and Bilodeau, G.- A. (2014). Watch-list screening using ensembles based on multiple face representations. In ICPR, pages 4489-4494.
  4. Bashbaghi, S., Granger, E., Sabourin, R., and Bilodeau, G.- A. (2015). Ensembles of exemplar-svms for video face recognition from a single sample per person. In AVSS, pages 1-6.
  5. Bashbaghi, S., Granger, E., Sabourin, R., and Bilodeau, G.- A. (2016). Robust watch-list screening using dynamic ensembles of svms based on multiple face representations. Machine Vision and Applications.
  6. Britto, A. S., Sabourin, R., and Oliveira, L. E. (2014). Dynamic selection of classifiers - a comprehensive review. Pattern Recognition, 47(11):3665 - 3680.
  7. Chang, C.-C. and Lin, C.-J. (2011). Libsvm: A library for support vector machines. ACM TIST, 2(3):1-27.
  8. Chen, C., Dantcheva, A., and Ross, A. (2015). An ensemble of patch-based subspaces for makeup-robust face recognition. Information Fusion, pages 1-13.
  9. De la Torre Gomerra, M., Granger, E., Radtke, P. V., Sabourin, R., and Gorodnichy, D. O. (2015). Partiallysupervised learning from facial trajectories for face recognition in video surveillance. Information Fusion, 24:31-53.
  10. De-la Torre Gomerra, M., Granger, E., Sabourin, R., and Gorodnichy, D. O. (2015). Adaptive skew-sensitive ensembles for face recognition in video surveillance.
  11. Pattern Recognition, 48(11):3385 - 3406.
  12. Deniz, O., Bueno, G., Salido, J., and la Torre, F. D. (2011). Face recognition using histograms of oriented gradients. Pattern Recognition Letters, 32(12):1598 - 1603.
  13. Dewan, M. A. A., Granger, E., Marcialis, G.-L., Sabourin, R., and Roli, F. (2016). Adaptive appearance model tracking for still-to-video face recognition. Pattern Recognition, 49:129 - 151.
  14. Huang, Z., Shan, S., Wang, R., Zhang, H., Lao, S., Kuerban, A., and Chen, X. (2015). A benchmark and comparative study of video-based face recognition on cox face database. IP, IEEE Trans on, 24(12):5967-5981.
  15. Kamgar-Parsi, B., Lawson, W., and Kamgar-Parsi, B. (2011). Toward development of a face recognition system for watchlist surveillance. IEEE Trans on PAMI, 33(10):1925-1937.
  16. Malisiewicz, T., Gupta, A., and Efros, A. (2011). Ensemble of exemplar-svms for object detection and beyond. In ICCV, pages 89-96.
  17. Mokhayeri, F., Granger, E., and Bilodeau, G.-A. (2015). Synthetic face generation under various operational conditions in video surveillance. In ICIP, pages 4052- 4056.
  18. Nourbakhsh, F., Granger, E., and Fumera, G. (2016). An extended sparse classification framework for domain adaptation in video surveillance. In ACCV, Workshop on Human Identification for Surveillance.
  19. Pagano, C., Granger, E., Sabourin, R., Marcialis, G., and Roli, F. (2014). Adaptive ensembles for face recognition in changing video surveillance environments. Information Sciences, 286:75-101.
  20. Pan, S. J. and Yang, Q. (2010). A survey on transfer learning. KDE, IEEE Trans on, 22(10):1345-1359.
  21. Patel, V., Gopalan, R., Li, R., and Chellappa, R. (2015). Visual domain adaptation: A survey of recent advances. IEEE Signal Processing Magazine, 32(3):53-69.
  22. Qiu, Q., Ni, J., and Chellappa, R. (2014). Dictionary-based domain adaptation for the re-identification of faces. In Person Re-Identification, Advances in Computer Vision and Pattern Recognition, pages 269-285.
  23. Shekhar, S., Patel, V., Nguyen, H., and Chellappa, R. (2013). Generalized domain-adaptive dictionaries. In CVPR, pages 361-368.
  24. Yang, M., Van Gool, L., and Zhang, L. (2013). Sparse variation dictionary learning for face recognition with a single training sample per person. In ICCV, pages 689-696.
Download


Paper Citation


in Harvard Style

Bashbaghi S., Granger E., Sabourin R. and Bilodeau G. (2017). Dynamic Selection of Exemplar-SVMs for Watch-list Screening through Domain Adaptation . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 738-745. DOI: 10.5220/0006256507380745


in Bibtex Style

@conference{icpram17,
author={Saman Bashbaghi and Eric Granger and Robert Sabourin and Guillaume-Alexandre Bilodeau},
title={Dynamic Selection of Exemplar-SVMs for Watch-list Screening through Domain Adaptation},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={738-745},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006256507380745},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Dynamic Selection of Exemplar-SVMs for Watch-list Screening through Domain Adaptation
SN - 978-989-758-222-6
AU - Bashbaghi S.
AU - Granger E.
AU - Sabourin R.
AU - Bilodeau G.
PY - 2017
SP - 738
EP - 745
DO - 10.5220/0006256507380745