Incremental Learning of Boosted Face Detector

In recent years, boosting has been successfully applied to many practical problems in pattern recognition and computer vision fields such as object detection and tracking. As boosting is an offline training process with beforehand collected data, once learned, it cannot make use of any newly arriving ones. However, an offline boosted detector is to be exploited online and inevitably there must be some special cases that are not covered by those beforehand collected training data. As a result, the inadaptable detector often performs badly in diverse and changeful environments which are ordinary for many real-life applications. To alleviate this problem, this paper proposes an incremental learning algorithm to effectively adjust a boosted strong classifier with domain-partitioning weak hypotheses to online samples, which adopts a novel approach to efficient estimation of training losses received from offline samples. By this means, the offline learned general-purpose detectors can be adapted to special online situations at a low extra cost, and still retains good generalization ability for common environments. The experiments show convincing results of our incremental learning approach on challenging face detection problems with partial occlusions and extreme illuminations.