This paper describes a study of the detection of human eyes without employing complicated procedures. Template matching is widely utilized in supervised algorithms, however, most of these methods employ a single searching window in a limited scope. The authors take advantage of symmetry with respect to the center of the facial image and propose a pair of windows as a template. This is useful to the symmetric structure of the image data, and helpful in handling a pair of objects at once. Three window-pair templates (circular, elliptic, and square) and a single elliptic window for comparison are considered in this study. Up to six parameters (in the case of elliptic and square window-pairs) are employed to adjust the window-pair.
Genetic Algorithm, based on the mechanics of probabilistic evolution, is employed to meet the multi-point search requirements without resorting to complex procedures. The algorithm, based on an unsupervised searching approach, is applied in evaluating the results from the differentiation of the image data. The differential image is used since the contours of the eye have a higher score in differentiation compared with other parts of the facial image. Some experimental simulations are made to confirm the proposed system's performance. The system is then adapted to investigate the window models listed above. The suitability of the different models in eye-detection was investigated by application to a set of images under identical Genetic Algorithm parameter settings. The detection capability of this method proved to be fairly reasonable without employing any deterministic approach, and the provided parameters in this system are easily handled regardless of the shapes of the faces in a sense.
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