Classifier ensembles with trajectory under-sampling for face re-identification
Classifier ensembles with trajectory under-sampling for face re-identification
Soleymani, Roghayeh and Granger, Eric and Fumera, Giorgio
ICPRAM 2016 – Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods 2016
Abstract : In person re-identification applications, an individual of interest may be covertly tracked and recognized based on trajectories of faces or other distinguishing information captured with video surveillance camera. However, a varying level of imbalance often exists between target and non-target facial captures, and this imbalance level may differ from what was considered during design. The performance of face classification systems typically declines in such cases because, to avoid bias towards the majority class (non-target), they tend to optimize the overall accuracy under a balance class assumption. Specialized classifier ensembles trained on balanced data, where non-target samples are selected through random under-sampling or cluster-based sampling, have been proposed in literature, but they suffer from loss of information and low diversity and accuracy. In this paper, a new ensemble method is proposed for generating a diverse pool of classifiers, each one trained on different levels of class imbalance and complexity for a greater diversity of opinion. Ensembles with Trajectory Under Sampling (EoC-TUS) allows to select subsets of non-target training data based on trajectories information. Variants of these ensembles can give more importance to the most efficient classifiers in identifying target samples, or define efficient and diverse decision boundaries by starting selection of trajectories from the farthest ones to the target class. For validation, experiments are conducted using videos captured in the Faces In Action dataset, and compared to several baseline techniques. The proposed EoC-TUS outperforms state-of-the-art techniques in terms of accuracy and diversity over a range of imbalance levels in the input video.
