We propose an algorithm for the detection of facial regions within input images. The characteristics of this algorithm are (1) a vast number of Gabor-type features (196,800) in various orientations, and with various frequencies and central positions, which are used as feature candidates in representing the patterns of an image, and (2) an information maximization principle, which is used to select several hundred features that are suitable for the detection of faces from among these candidates. Using only the selected features in face detection leads to reduced computational cost and is also expected to reduce generalization error. We applied the system, after training, to 42 input images with complex backgrounds (Test Set A from the Carnegie Mellon University face data set). The result was a high detection rate of 87.0%, with only six false detections. We compared the result with other published face detection algorithms.

The energy model (Pollen & Ronner, 1983; Adelson & Bergen, 1985) for a complex cell in the visual cortex is investigated theoretically. The energy model describes the output of a complex cell as the squared sum of outputs of two linear operators. An information-maximization problem to determine the two linear operators is investigated assuming the low signal-to-noise ratio limit and a localization term in the objective function. As a result, two linear operators characterized by a quadrature pair of Gabor functions are obtained as solutions. The result agrees with the energy model, which well describes the shift-invariant and orientation-selective responses of actual complex cells, and thus suggests that complex cells are optimally designed from an information-theoretic viewpoint.