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).
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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
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