Abstract --- This paper presents a novel Bayesian Discriminating Features (BDF) method for multiple frontal face detection. The BDF method, which is trained on images from only one database yet works on test images from diverse sources, displays robust generalization performance. The novelty of this paper comes from the integration of the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1-D Harr wavelet representation, and its amplitude projections. While the Harr wavelets produce an effective representation for object detection, the amplitude projections capture the vertical symmetric distributions and the horizontal characteristics of human face images. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. While the face class is usually modeled as a multivariate normal distribution, the nonface class is much more difficult to model due to the fact that it includes ``the rest of the world''. The estimation of such a broad category is, in practice, intractable. However, one can still derive a subset of the nonfaces that lie closest to the face class, and then model this particular subset as a multivariate normal distribution. Finally, the Bayes classifier applies the estimated conditional PDFs to detect multiple frontal faces in an image. Experimental results using 887 images (containing a total of 1,034 faces) from diverse image sources show the feasibility of the BDF method. In particular, the novel BDF method achieves 98.5% face detection accuracy with one false detection.
Index Terms --- Bayes classifier, Bayesian Discriminating Features (BDF), discriminating feature analysis, face detection, statistical modeling, support nonfaces
Experimental Results of Face Detection
