Multi-view face detection in videos with online adaptation
Most learning-based approaches to face detection suffer from the problem of performance degradation on faces that are not covered by training data. However, including all variations of faces in training is practically infeasible due to the scalability restriction of machine learning algorithms and expensive manual labeling. In this work, we focus on face detection in videos, and alleviate this problem by exploiting strong correlation among video frames. We augment a pre-trained multiview face detection with an incrementally derived Gaussian process regressor. The regressor can extract and propagate visual knowledge across frames, and adapts the detector to handle unseen faces. Testing on two datasets, the promising results manifest the effectiveness of the proposed approach.