Boosting-based methods have recently led to the state-of-the-art face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the boosting process, the non-face examples collected by bootstrapping become very similar to the face examples, and the classification error of Haar-like feature-based weak classifiers is thus very close to 50\%. As a result, the performance of a face detector cannot be further improved. This paper proposed a solution to this problem, introducing a face detection method based on boosting in hierarchical feature spaces (both local and global). We argue that global features, like those derived from Principal Component Analysis, can be advantageously used in the later stages of boosting, when local features do not provide any further benefit, without affecting computational complexity. We show, based on statistics of face and non-face examples, that weak classifiers learned in hierarchical feature spaces are better boosted. Our methodology leads to a face detection system that achieves higher performance than the current state-of-the-art system, at a comparable speed.