26-06-2012, 05:10 PM
SEMINAR ON EYE BALL TRACKING
[attachment=25650]
Introduction
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:
• RealTime face tracking with scale and rotation invariance
• Tracking the eye areas individually
• Tracking eye features
• Eye gaze direction finding
• Remote controlling using eye movements
Eye Detection
Two different methods were implemented in the project:
1. Template-Matching
2. Adaptive EigenEye Method
Template-Matching
Template-Matching is a well-known method for object detection. In our template matching method, a standard eye pattern is created manually and given an input image, the correlation values with the standard patterns are computed for the eyes. The existence of an eye is determined based on the correlation values. This approach has the advantage of being simple to implement. However, it may sometimes be inadequate for eye detection since it cannot effectively deal with variation in scale, pose and shape.
Adaptive EigenEye Method
Adaptive EigenEye Method is based on the well-known method EigenFaces. However as the method is used for eye detection we named it as “EigenEye Method”. The main idea is to decompose eye images into a small set of characteristics feature images called eigeneyes, which may be thought of as the principal components of the original images. These eigeneyes function as the orthogonal basis vectors of a subspace called eyespace. However we know that the eigenface method is not scale invariant. To provide the scale invariance we can resize the eye-database once with the information gathered by the face detection algorithm (EyeWidth / FaceWidth ? 0.35), we can provide scale-invariant detection using only one database.
[attachment=25650]
Introduction
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:
• RealTime face tracking with scale and rotation invariance
• Tracking the eye areas individually
• Tracking eye features
• Eye gaze direction finding
• Remote controlling using eye movements
Eye Detection
Two different methods were implemented in the project:
1. Template-Matching
2. Adaptive EigenEye Method
Template-Matching
Template-Matching is a well-known method for object detection. In our template matching method, a standard eye pattern is created manually and given an input image, the correlation values with the standard patterns are computed for the eyes. The existence of an eye is determined based on the correlation values. This approach has the advantage of being simple to implement. However, it may sometimes be inadequate for eye detection since it cannot effectively deal with variation in scale, pose and shape.
Adaptive EigenEye Method
Adaptive EigenEye Method is based on the well-known method EigenFaces. However as the method is used for eye detection we named it as “EigenEye Method”. The main idea is to decompose eye images into a small set of characteristics feature images called eigeneyes, which may be thought of as the principal components of the original images. These eigeneyes function as the orthogonal basis vectors of a subspace called eyespace. However we know that the eigenface method is not scale invariant. To provide the scale invariance we can resize the eye-database once with the information gathered by the face detection algorithm (EyeWidth / FaceWidth ? 0.35), we can provide scale-invariant detection using only one database.