Real-time detection and tracking of human eyes in video sequences

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2005

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Savaş, Zafer

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Robust, non-intrusive human eye detection problem has been a fundamental and challenging problem for computer vision area. Not only it is a problem of its own, it can be used to ease the problem of finding the locations of other facial features for recognition tasks and human-computer interaction purposes as well. Many previous works have the capability of determining the locations of the human eyes but the main task in this thesis is not only a vision system with eye detection capability; Our aim is to design a real-time, robust, scale-invariant eye tracker system with human eye movement indication property using the movements of eye pupil. Our eye tracker algorithm is implemented using the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm proposed by Bradski and the EigenFace method proposed by Turk & Pentland. Previous works for scale invariant object detection using Eigenface method are mostly dependent on limited number of user predefined scales which causes speed problems; so in order to avoid this problem an adaptive eigenface method using the information extracted from CAMSHIFT algorithm is implemented to have a fast and scale invariant eye tracking. First of all; human face in the input image captured by the camera is detected using the CAMSHIFT algorithm which tracks the outline of an irregular shaped object that may change size and shape during the tracking process based on the color of the object. Face area is passed through a number of preprocessing steps such as color space conversion and thresholding to obtain better results during the eye search process. After these preprocessing steps, search areas for left and right eyes are determined using the geometrical properties of the human face and in order to locate each eye indivually the training images are resized by the width information supplied by the CAMSHIFT algortihm. Search regions for left and

Subject Keywords

Electrical engineering.

URI

http://etd.lib.metu.edu.tr/upload/12606459/index.pdf
https://hdl.handle.net/11511/15424

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

Z. Savaş, “Real-time detection and tracking of human eyes in video sequences,” M.S. - Master of Science, Middle East Technical University, 2005.