Skew-sensitive boolean combination for adaptive ensembles - An application to face recognition in video surveillance

摘要

Several ensemble-based techniques have been proposed to design pattern recognition systems when data has imbalanced class distributions, although class proportions may change over time according to the operational environment. For instance, in video surveillance applications, face recognition (FR) is employed to detect the presence of target individuals of interest in potentially complex and changing environments. Systems for FR in video surveillance are typically designed a priori with a limited amount of reference target data and prior knowledge of underlying class distributions. However, the relatively proportion of target and non-target faces captured during operations varies over time. Estimating the actual proportion of data from the input data stream could allow to dynamically adapt ensembles to reflect operational conditions. In this paper, the selection and fusion of ensembles produced through Boolean Combination (BC) of classifiers is periodically adapted based on the class proportions estimated from input streams. BC techniques have been shown to efficiently integrate the responses of multiple diversified classifiers in the ROC space, yet the impact on performance of imbalanced data distributions is difficult to observe from ROC curves. Given a diversified pool of classifiers and a desired false positive rate (fpr), the new Skew-Sensitive Boolean Combination (SSBC) technique exploits the Precision-Recall Operating Characteristic (PROC) space, leading to a higher level of performance. A set of BCs of base classifiers is initially produced with imbalanced reference data in the PROC space, where each BC curve corresponds to different level of imbalance (a growing number of non-target samples versus a fixed number of target ones). Then, during operations, the closest adjacent levels of class imbalance are 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 from operational points of these curves. Simulation results on Faces In Action video surveillance data indicate that ensemble-based FR systems using the SSBC technique outperform the same systems using traditional BC techniques with Random Under-Sampling and One-Sided Selection. It allows to dynamically select BCs that provide a higher level of precision (and F1 value) for target individuals, and a significantly smaller difference between desired and actual fpr. Performance of this adaptive approach is also comparable to the costly full recalculation of BCs (as required by a BC technique to accommodate a specific level of imbalance), but for a computational complexity that is considerably lower. Finally, SSBC is shown to achieve a high level of discrimination between target and non-target individuals when face tracking is exploited to accumulate ensemble predictions for facial captures that correspond to a same person in the video scene.