Progressive boosting for class imbalance and its application to face re-identification.

•The progressive Boosting Ensemble is proposed for learning from imbalanced data.•Partitioning data in Boosting lead to higher diversity and less information loss.•Trajectory under-sampling in PBoost is more effective for face re-identification.•Validating on various skew levels of data in Boosting increases robustness to skew.•Partitioning and validating on different skew levels reduce computation complexity.