Abstract
Systems for face re-identification over a network of video surveillance cameras are designed with a limited amount of reference data, and may operate under complex environments. Furthermore, target individuals provide a small proportion of the facial captures for design and during operations, and these proportions may change over time according to operational conditions. Given a diversified pool of base classifiers and a desired false positive rate (fpr), the Skew-Sensitive Boolean Combination (SSBC) technique allows to adapt the selection of ensembles based on changes to levels of class imbalance, as estimated from the input video stream. Initially, a set of BCs for the base classifiers is produced in the ROC space, where each BC curve corresponds to reference data with a different level of imbalance. Then, during operations, class imbalance is periodically estimated using the Hellinger distance between the data distribution of inputs and that of imbalance levels, and used to approximate the most accurate BC of classifiers among operational points of these curves viewed in the precision-recall space. Simulation results on real-world video surveillance data indicate that, compared to traditional approaches, FR systems based on SSBC allow to select BCs that provide a higher level of precision for target individuals, and a significantly smaller difference between desired and actual fpr. Performance of this adaptive approach is also comparable to full recalculation of BCs (for a specific level of imbalance), but for a considerably lower complexity. Using face tracking, a high level of discrimination between target and non-target individuals may be achieved by accumulating SSBC predictions for faces captured corresponding to a same track in video footage.
This work was supported by the Natural Sciences and Engineering Research Council of Canada, and the Centre for Security Science (Defense R&D Canada).
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
Brew, A., Cunningham, P.: Combining cohort and ubm models in open set speaker detection. Multimedia Tools Appl. 48, 141–159 (2010)
Connolly, J.-F., Granger, E., Sabourin, R.: Evolution of Heterogeneous Ensembles Through Dynamic Particle Swarm Optimization for Video-Based Face Recognition. Pattern Recognition 45(7), 2460–2477 (2012)
Davis, J., Goadrich, M.: The Relationship Between Precision-Recall and ROC Curves. In: Proceedings of the 23rd International Conference on Machine Learning, New York, NY, USA, pp. 233–240 (2006)
Ekenel, H.K., Stallkamp, J., Stiefelhagen, R.: A video-based door monitoring system using local appearance-based face models. Comput. Vis. Image Underst. 114, 596–608 (2010)
Ekenel, H.K., Szasz-Toth, L., Stiefelhagen, R.: Open-set face recognition-based visitor interface system. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 43–52. Springer, Heidelberg (2009)
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(4), 463–484 (2012)
Goh, R., Liu, L., Liu, X., Chen, T.: The CMU face in action (FIA) database. In: Zhao, W., Gong, S., Tang, X. (eds.) AMFG 2005. LNCS, vol. 3723, pp. 255–263. Springer, Heidelberg (2005)
González-Castro, V., Alaiz-Rodríguez, R., Fernández-Robles, L., Guzmán-Martínez, R., Alegre, E.: Estimating Class Proportions in Boar Semen Analysis Using the Hellinger Distance. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010, Part I. LNCS, vol. 6096, pp. 284–293. Springer, Heidelberg (2010)
Granger, E., Khreich, W., Sabourin, R., Gorodnichy, D.O.: Fusion of Biometric Systems Using Boolean Combination: An Application to Iris-Based Authentication. International Journal on Biometrics 4(3), 291–315 (2012)
Kamgar-Parsi, B., Lawson, W., Kamgar-Parsi, B.: Toward development of a face recognition system for watchlist surveillance. TPAMI 33, 1925–1937 (2011)
Landgrebe, T.C.W., Paclik, P., Duin, R.P.W., Bradley, A.P.: Precision-Recall Operating Characteristic (P-ROC) Curves in Imprecise Environments. In: 18th International Conference on Pattern Recognition, pp. 123–127 (2006)
Li, F., Wechsler, H.: Open set face recognition using transduction. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(11), 1686–1697 (2005)
Matta, F., Dugelay, J.-L.: Person recognition using facial video information: A state of the art. J. Vis. Lang. Comput. 20, 180–187 (2009)
Pagano, C., Granger, E., Sabourin, R., Gorodnichy, D.O.: Detector Ensembles for Face Recognition in Video Surveillance. In: Proc. 2012 Int’l Joint Conf. on Neural Networks, Brisbane, Australia, pp. 1–8 (2012)
Satta, R., Fumera, G., Roli, F.: Fast Person Re-Identification Based on Dissimilarity Representations. Pattern Recognition Letters 33(14), 1838–1848 (2012)
Scott, M., Niranjan, M., Prager, R.W.: Realisable classifiers: Improving operating performance on variable cost problems. In: Proc. British MV Conf. (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Radtke, P., Granger, E., Sabourin, R., Gorodnichy, D. (2013). Adaptive Ensemble Selection for Face Re-identification under Class Imbalance. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-38067-9_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38066-2
Online ISBN: 978-3-642-38067-9
eBook Packages: Computer ScienceComputer Science (R0)