Browse > Article

Real Time 3D Face Pose Discrimination Based On Active IR Illumination  

박호식 (관동대학교)
배철수 (관동대학교)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.8, no.3, 2004 , pp. 727-732 More about this Journal
Abstract
In this paper, we introduce a new approach for real-time 3D face pose discrimination based on active IR illumination from a monocular view of the camera. Under the IR illumination, the pupils appear bright. We develop algorithms for efficient and robust detection and tracking pupils in real time. Based on the geometric distortions of pupils under different face orientations, an eigen eye feature space is built based on training data that captures the relationship between 3D face orientation and the geometric features of the pupils. The 3D face pose for an input query image is subsequently classified using the eigen eye feature space. From the experiment, we obtained the range of results of discrimination from the subjects which close to the camera are from 94,67％, minimum from 100％, maximum.
Keywords
Pupil Detection; Pupil Tracking; Face Pose Discrimination;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Blake, R. Curwen, and A. Zisserman. A framework for spatio-temporal control in the tracking of visual contours, Int. Journal of Computer Vision, 11(2):127-145, 1993   DOI   ScienceOn
2 S. Gong, E. Ong, and S. McKenna. Leaming to associate faces across views in vector space of similarities of prototypes. Proc of BriHsh Machine Vision, 1998
3 T. Horprasert, Y. Yacoob, and L. Davis. Computing 3d head orientation from a monocular image. Proc. of Int. Conf. on automatic face and gesture recognition, pages 242 247, 1996
4 A. Gee and R. Cipoll. Determining the gaze of faces in images, Image and Vision Computing, 30:639 647, 1994
5 A. Pentland, B. Moghaddam, and T. Stamer. View based and modular eigenspaces for face recognition. CVPR 94, pages 84-91, 1994
6 T. E. Hutchinson. Eye movement detection with improved calibration and speed. U.S. patent 4950069, April, 1990
7 R. Rae and H. Ritter. Recognition of human head orientation based on artiHcial neural networks. IEEE Transactions on Neural Networks, 9(2):257 265, 1998   DOI   ScienceOn
8 A. Tsukamoto, C. Lee, and S. Tsuji. Detection and pose estimation of human face with synthesized image models. ICPR 94, pages 754 757, 1994