Efficiently training a better visual detectorwith sparse eigenvectors

Face detection plays an important role in many vision applications. Since Viola and Jones [1] proposed the first real-time AdaBoost based object detection system, much ef- fort 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 ob- ject detector. In particular, we have adopted Greedy Sparse Linear Discriminant Analysis (GSLDA) [2] for its computa- tional efficiency; and slightly better detection performance is achieved compared with [1]. Moreover, we propose a new technique, termed Boosted Greedy Sparse Linear Dis- criminant Analysis (BGSLDA), to efficiently train object de- tectors. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distri- butions, e.g., face detection, demonstrates that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportu- nity 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.