Adaptive cascade of boosted ensembles for face detection in concept drift

We propose an adaptive learning algorithm for cascades of boosted ensembles that is designed to handle the problem of concept drift in nonstationary environments. The goal was to create a real-time adaptive algorithm for dynamic environments that exhibit varying degrees of drift in high-volume streaming data. This we achieved using a hybrid of detect-and-retrain and constant-update approaches. The uniqueness of our method is found in two aspects of our framework. The first is the manner in which individual weak classifiers within each cascade layer of an ensemble are clustered during training and assigned a competence value. Secondly, the idea of learning optimal cascade-layer thresholds during runtime, which enables rapid adaptation to dynamic environments. The proposed adaptive learning method was applied to a binary-class problem with rare-event detection characteristics. For this, we chose the domain of face detection and demonstrate experimentally the ability of our algorithm to achieve an effective trade-off between accuracy and speed of adaptations in dense data streams with unknown rates of change.