Boosted Gaussian Classifier with Integral Histogram for Face Detection

Novel features and weak classifiers are proposed for face detection within the AdaBoost learning framework. Features are histograms computed from a set of spatial templates in filtered images. The filter banks consist of Intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming at capturing spatial and frequency properties of faces at different scales and orientations. Features selected by AdaBoost learning, each of which is corresponding to a histogram with a pair of filter and template, can thus be interpreted as boosted marginal distributions of faces. We fit the Gaussian distribution of each histogram feature only for positives (faces) in the sample set as the weak classifier. The results of experiment demonstrate that classifiers with corresponding features are more powerful to describe the face pattern than haar-like rectangle features introduced by Viola and Jones.