The Problem

The Problem

Sana Naghipour, Saba Naghipour, Phani Chavali , Ed Richter, and Arye Nehorai
Preston M. Green Department of Electrical and Systems Engineering

The Eye tracker project is a research initiative to enable people,
who are suffering from Amyotrophic Lateral Sclerosis (ALS), to
use prosthetic limbs using their eyes by tracking the movement
of the pupil. The project will be implemented in two main
phases. The idea is to mount an infrared camera onto a pair of
sunglasses and capture the movement of the pupil, and move
the limbs using the control signal generated, based on the pupil
movements. In this semester, we focused on developing
software tools for tracking the motion of the eye. In the next
semester, we will build the hardware necessary to control the
prosthetic limbs.

To track the location of the pupil, in a live video stream using
image processing techniques.

Experimental Setup

Procedure Description
We implement our project in the following steps.

Image Acquisition

We use Labview to capture the video using an infrared camera. There is
support for recording of videos with several frame rates, and formats.
After obtaining the video, we perform sequential frame by frame

Discarding Color information

We convert the images from all the frames into to their corresponding
gray scale images. To do this, we average the pixel values in all the
three color channel to obtain a gray scale image.

Low pass filtering

b) Labview instrument

a) Experimental Setup

We use low-pass filtering to remove the sharp edges in each image.
This also helps to remove the undesired background light in the

Some Examples

First Phase : development of the software for pupil tracking
Second Phase : building the hardware necessary to capture
the images of the eye and transfer the images to a processing
Help ALS patients in various tasks such as communication,
writing emails, drawing, making music.
The challenges include but are not limited to developing
algorithms that are: (i) fast, which obtain good frame rate (ii)
robust, that are insensitive to lighting conditions and facial

We scale down the filtered images to obtain lower resolution images.
This serves two purposes. First, since the dimension of the image
decreases, scaling improves the processing time. Second, the
averaging effect removes the undesired background light.

Template Matching

We used a template matching algorithm to segment the darkest region of
the image. Since after discarding the color information and, low-pass
filtering, the pupil corresponds to the darkest spot in the eye, this
method was used. We used a small patch of dark pixels as a
template. The matching is done using exhaustive search over the
entire image. Once a match is found, the centroid of the this block
was determined to the pupil location. For the experiments, we used a
block size of 5 x 5 pixels.

The picture is taken from the website: The image shows pair of infrared
cameras mounted on sunglasses to detect the pupil
movements of the patient.


Determining the search space

Since the exhaustive search over the entire image to find a match is
computationally intensive, we propose an adaptive search method.
Using this method, we choose the search space based on the pupil
location from earlier frame. In this manner, using the past information,
we were able to greatly reduce the complexity of the search. We
used a search space of 75 X 75 pixels around the pupil location from
the last frame.

An algorithm for estimating the position and the movement of the pupil is
implemented using a template matching method. In the future, we will build the
necessary hardware that uses the algorithm for prosthetic limb control.



P. W. Hallinan, Recognizing human eyes, in: SPIE Proceedings,Vol. 1570:
Geometric Methods in Computer Vision, 1991, pp. 212{226.

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