Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers

Face-detection methods based on cascade architecture have demonstrated a fast and robust performance. In most of these methods, each node of the cascade employs the simple Haar-like features from the central eye–nose–mouth region using the boosting method. However, it can be empirically observed that, in the deeper nodes of the boosting process, the non-face examples collected by bootstrapping are in fact very similar to the face examples, and the error rate of those feature-based weak classifiers is very close to 50%. Consequently, the performance of the face detector is hardly further improved. In this paper, we propose a novel and simple solution to this problem by imitating the characteristics of the human visual system. The main idea of our solution is to boost the cascade based on a hierarchical strategy, which employs the information from the central and surrounding parts of the face regions step by step. We argue that the context information about a face can be advantageously used in the deeper nodes of the boosting process when the features derived from the central region of the face do not provide any further benefit. Furthermore, we also propose a simplified Gabor feature to extend the feature set for the training of deeper nodes. Experiments show that our proposed method can improve not only the detection performance, but also the detection speed, by about 10% when compared to the original AdaBoost face-detection method for our implementation.