Gaze Detection Using Convolutional Neural Network for Interactive Displays
Date Issued
2015
Date
2015
Author(s)
Chen, Yu-Ting
Abstract
Many new interactive display devices appear recently, like Google Glass, Oculus, Samsung TV and so on. For large interactive display, like a wall, gaze-based interaction can be more effective and convenient. However, many gaze detection system need intrusive light, wearable devices or fixed head pose. In this paper, our goal is to study if head pose information can be useful for gaze detection. We propose a method which uses RGB-D camera for head pose detection and high esolution camera for gaze detection. The main idea is applying the new technology named Convolutional Neural Network (CNN) as the training process. We compared accuracy of gaze detection for interactive display between two well-known models of CNN with three approaches. We held an experiment on an interactive wall to collect data for our approach. The result shows our system can have more than 80% accuracy for 36 labels gaze detection. The head pose information provided no significant improvement. Even then, our approach still has good accuracy.
Subjects
Gaze Detection
Convolutional Neural Network
Interactive Displays
Computer Vision
Human Computer Interaction
Type
thesis
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