TY - GEN
T1 - Facial and eye gaze detection
AU - Park, Kang Ryoung
AU - Lee, Jeong Jun
AU - Kim, Jaihie
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2002
Y1 - 2002
N2 - Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye’s movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.8 cm of RMS error.
AB - Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye’s movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.8 cm of RMS error.
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U2 - 10.1007/3-540-36181-2_37
DO - 10.1007/3-540-36181-2_37
M3 - Conference contribution
AN - SCOPUS:23044532089
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 368
EP - 376
BT - Biologically Motivated Computer Vision - 2nd International Workshop, BMCV 2002, Proceedings
A2 - Bulthoff, Heinrich H.
A2 - Wallraven, Christian
A2 - Lee, Seong-Whan
A2 - Poggio, Tomaso A.
PB - Springer Verlag
T2 - 2nd International Workshop on Biologically Motivated Computer Vision, BMCV 2002
Y2 - 22 November 2002 through 24 November 2002
ER -