Eye feature extraction consists of two stages. In the first stage, also known as eye detection, the eye perimeter is extracted from the face image. In the next stage, the interest region has been searched for localizing the feature point(s). Many appropriated algorithms have been proposed in the literatures ,  for eye detection. Here, we concentrate on localizing features in this perimeter.
Fig. 5. Result of performing LPT on the left eye image (59×91 pixels). Two maximums are placed on the top and bottom eyelid.
In order to locate the exact iris, first, the eye searching perimeter is extracted from the face image. Afterward, this area is divided into the left and right eye searching perimeter to increase algorithm accuracy. Finally, the proposed method is applied to each perimeter. Fig. 5 shows the result of utilizing LPT on the left eye searching perimeter. Features localization algorithm is as follows:
Feature Localization Algorithm
1. Extract the eye searching perimeter from face image and name it “eye”.
eye = eye_detect(fac...
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