Machine Learning
Digital Image Processing
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A fast and robust 3D head pose and gaze estimation system
Proceedings of the 8th international conference on Multimodal interfaces
Journal of Cognitive Neuroscience
Efficiency, Trust, and Visual Appeal: Usability Testing through Eye Tracking
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Robust face detection using Gabor filter features
Pattern Recognition Letters
Probabilistic multiple face detection and tracking using entropy measures
IEEE Transactions on Circuits and Systems for Video Technology
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There have been many recent studies on gaze recognition in the field of Human-Computer Interaction (HCI). Gaze recognition and other biomedical signals will be a very natural and intuitive part of Human-Computer Interaction. In studies on gaze recognition, identifying the user is the most applicable task, and it has had a lot of attention from many different studies. Most existing research on gaze recognition has problems with universal use because the process requires a head-mounted infrared Light Emitting Diode (LED) and a camera, both expensive pieces of equipment. Cheaper alternatives like webcams have the disadvantage of poor recognition performance. This paper proposes and implements the Support Vector Machine-based (SVM) gaze recognition system using one webcam and an advanced eye region detection method. In this paper, we detected the face and eye regions using Haar-like features and the AdaBoost learning algorithm. Then, we used a Gabor filter and binarization for advanced eye region detection. We implemented a Principal Component Analysis (PCA) and Difference Image Entropy-based (DIE) gaze recognition system for the performance evaluation of the proposed system. In the experimental results, the proposed system shows 97.81% recognition of 4 directions, 92.97% recognition of 9 directions, demonstrating its effectiveness.