Eye detection in a face image using linear and nonlinear filters

Abstract This paper describes two methods of eye detection in a face image. The face is first detected as a large flesh-colored region, and anthropometric data are then used to estimate the size and separation of the eyes. When a linear filtering method, using filters based on Gabor wavelets, was then applied to detect the eyes in the gray-level image of the face, the detection rate was good (80% on one dataset, 95% on another), but there were many false alarms. A nonlinear filtering method was therefore developed to detect the corners of the eyes in the color image of the face. This method gave a 90% detection rate with no false alarms.

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