Spectral histogram based face detection
Christopher A. Waring, Xiuwen Liu · 2004
This paper adopts a generic feature representation and applies it to the task of face detection as an appearance-based case. The distribution of faces and non-faces are modeled from the marginal distribution of filter responses. The face detection algorithm proposed here uses the spectral representation of a 21/spl times/21 image window as input to a multiple layer perceptron for classification. The classifier is trained with the backpropagation learning rule. A simple method to correct nonuniform illuminance is used to normalize all training and test images. Testing is done on a standard data set and compared to the work of others.