Face detection using data and sensor fusion techniques
A new face detection method based on learning is proposed in this paper, it has three properties: first, it uses not only the local facial feature but also the global facial feature to design weak classifiers, a new kind of global facial feature called as the unified average face feature (UAFF) is proposed; second, it uses two kinds of rectangle feature as the local feature, different from other methods, these local features are selected and calculated only in the partial regions of face; third, these weak classifiers corresponding to the global facial features and the local facial features are combined and trained by our novel cascade classifier training algorithm to construct a cascade face detector. Because of these properties, our face detector is robust and generalizes well. Experimental results show that, with a small number of features, it can reach higher detection rate while maintain lower false alarm rate. Moreover, it can detect faces with partial occlusion.