3 Locating Eye Features Using LPT
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 [16], [17] 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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Blank facial expressions what do we call them, eyes that pierce through glass and straight lined lips. This face is called a Poker Face a commonly heard term in our culture that has many uses and meanings. You see it in the spy movies that one man who has a blank expression so the enemy can’t see his plans or emotions. The accepted definition of poker face is an expressionless face stated by dictionary.com. Even though poker face is a blank facial expression there is so much more to the term that