Examples of the coded rules used to locate face candidates in the lowest resolution include: “the center part of the face (the dark shaded parts in Fig. 2) has four cells with a basically uniform intensity,” “the upper round part of a face (the light shaded parts in Fig. 2) has a basically uniform intensity,” and “the difference between the average gray values of the center part and the upper round part is significant.” The lowest resolution (Level 1) image is searched for face candidates and these are further processedat finer resolutions. At Level 2, local histogram equalization is performed on the face candidates received from Level 2, followed by edge detection. Surviving candidate regions are then examined at Level 3 with another set of rules that respond to facial features such as the eyes and mouth. Evaluated on a test set of 60 images, this system located faces in 50 of the test images while there are 28 images in which false alarms appear. One attractive feature of this method is that a coarse-to-fine or focus-of-attention strategy is used to reduce the required computation. Although it does not result in a high detection rate, the ideas of using a multiresolution hierarchy and rules to guide searches have been used in later face detection works