TrackEye Review

Detailed Description:

Eyes are the most important features of the human face. So effective usage of eye movements as a communication technique in user-to-computer interfaces can find place in various application areas.

Eye tracking and the information provided by the eye features have the potential to become an interesting way of communicating with a computer in a human-computer interaction (HCI) system. So with this motivation, designing a real-time eye feature tracking software is the aim of this project.


The purpose of the project is to implement a real-time eye-feature tracker with the following capabilities:

Instructions to Run and Rebuild TrackEye

Extract file. Before running TrackEye_636.exe, copy the two files SampleHUE.jpg and SampleEye.jpg to the C:\ folder. These two files are used for CAMSHIFT and Template-Matching algorithms.

There are no other steps to be followed by the user to run the software. There are no DLL dependencies as the software was built with the DLLs statically included.

Settings to be Done to Perform a Good Tracking

Settings for Face & Eye Detection

Under TrackEye Menu --> Tracker Settings.

Check “Track eyes in details” and then check “Detect also eye pupils”. Click “Adjust Parameters” button:

Check “Indicate eye boundary using active snakes”. Click “Settings for snake” button:

Special Equipment Based Approaches

These type of studies use the necessary equipment which will give a signal of some sort which is proportional to the position of the eye in the orbit. Various methods that are current in use are Electrooculography, Infra-Red Oculography, Scleral search coils. These methods are completely out of our project.

Image Based Approaches

Image based approaches perform eye detections on the images. Most of the image based methods try to detect the eyes using the features of the eyes. Methods used so far are knowledge-based methods, feature-based methods (color, gradient), simple template matching, appearance methods. Another interesting method is “Deformable template matching” which is based on matching a geometrical eye template on an eye image by minimizing the energy of the geometrical model.

Implementation of TrackEye

The implemented project is on three components:

Two different methods were implemented in the project. They are:


Haar-Face Detection Method

The second face detection algorithm is based on a classifier working with Haar-Like features (namely a cascade of boosted classifiers working with Haar-like features). First of all it is trained with a few hundreds of sample views of a face. After a classifier is trained, it can be applied to a region of interest in an input image. The classifier outputs a "1" if the region is likely to show face and "0" otherwise. To search for the object in the whole image, one can move the search window across the image and check every location using the classifier. The classifier is designed so that it can be easily "resized" in order to be able to find the objects of interest at different sizes, which is more efficient than resizing the image itself.