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A neural network method for accurate face detection on arbitrary images

In this paper we present a neural detector of frontal faces in gray scale images under arbitrary face size, orientation, facial expression, skin color, lighting conditions and background environment. In a two-level process, a window normalization module reduces the variability of the features and a neural classifier generates multiple face position hypotheses. Extended experiments carried out in a test-bed of 6406 face images, have shown that the face detection accuracy is increased significantly when non-linear and probabilistic illumination equalizers pre-process the sub-images. Moreover, better results can be achieved in case of training the neural detector using positional and orientation normalized face examples. In this case the neural face detector has the capability to locate both position and orientation of a face. In the multiple face position hypotheses generated by the proposed neural method, 98.3% detection accuracy, the highest reported in the literature, was measured.