Efficiently training a better visual detector with sparse eigenvectors

Face detection plays an important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based object detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we have adopted greedy sparse linear discriminant analysis (GSLDA) for its computational efficiency; and slightly better detection performance is achieved compared with. Moreover, we propose a new technique, termed boosted greedy sparse linear discriminant analysis (BGSLDA), to efficiently train object detectors. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions, e.g., face detection, demonstrates that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportunity to argue that Adaboost and similar approaches are not the only methods that can achieve high classification results for high dimensional data such as object detection.