Browse > Article

Pose-invariant Face Recognition using a Cylindrical Model and Stereo Camera  

노진우 (삼성전자)
홍정화 (고려대학교 제어계측공학)
고한석 (고려대학교 전자컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.7, 2004 , pp. 929-938 More about this Journal
Abstract
This paper proposes a pose-invariant face recognition method using cylindrical model and stereo camera. We divided this paper into two parts. One is single input image case, the other is stereo input image case. In single input image case, we normalized a face's yaw pose using cylindrical model, and in stereo input image case, we normalized a face's pitch pose using cylindrical model with previously estimated pitch pose angle by the stereo geometry. Also, since we have an advantage that we can utilize two images acquired at the same time, we can increase overall recognition performance by decision-level fusion. Through representative experiments, we achieved an increased recognition rate from 61.43% to 94.76% by the yaw pose transform, and the recognition rate with the proposed method achieves as good as that of the more complicated 3D face model. Also, by using stereo camera system we achieved an increased recognition rate 5.24% more for the case of upper face pose, and 3.34% more by decision-level fusion.
Keywords
face recognition; face pose; cylindrical model; stereo camera;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Pentland, B. Moghanddam, and T. Starner, 'View-based and modular eigenspaces for face recognition,' in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 84-91, Seattle, Washington, June 1994   DOI
2 F. J. Huang, Z. Zhou, H. Zhang, and T. Chen, 'Pose invariant face recognition,' in Proc. of IEEE Conf. on Automatic Face and Gesture Recognition, pp. 245-250, Grenoble, France, 2000   DOI
3 T. S. Jebara, '3D Pose estimation and normalization for face recognition,' McGill University, 1996
4 D. J. Beymer, 'Face recognition under varying pose,' in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 556-761, Seattle, Washington, June 1994   DOI
5 C. G. Feng, P. C. Yuen, and D. Q. Dai, 'A novel method for face orientation determination in human face recognition,' in Proc. of The Fourth Joint Conf. on Information Science, pp. 295-298, 1998
6 S. Akamatsu, T. Sasaki, H. Fukumachi, and Y. Suenaga, 'A robust face identification scheme KL expansion of an invariant feature space,' SP1E Proc., vol. 1607, pp. 71-84, Nov 1991   DOI
7 D. Graham and N. Allinson, 'Face recognition from unfamiliar views: Subspace methods and pose dependency,' in Proc. of IEEE Conf. on Automatic Face and Gesture Recognition, pp. 348-353, Nara, Japan, April 1998   DOI
8 M. Kirby and L. Sirovich, 'Application of the karhunen-loeve procedure for the characterization of human faces,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 103-108, Jan 1990   DOI   ScienceOn
9 M. H. Yang, D. Kriegman, and N. Ahuja, 'Detecting Faces in Images: A Survey,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan 2002   DOI   ScienceOn
10 T. Horprasert, Y. Yacoob, and L. Davis, 'Computing 3-D head orientation from a monocular image sequence,' in Proc. of IEEE Conf. on Automatic Face and Gesture Recognition, pp. 242-247, Killington, VT, Oct 1996   DOI
11 M. Xu and T. Akatsuka, 'Detecting head pose from stereo image sequence for active face recognition,' in Proc. of IEEE Conf. on Automatic Face and Gesture Recognition, pp, 82-87, Nara, Japan, April 1998   DOI
12 R. Hartley and A. Zisserman, Multiple View Geometry in computer vision, Cambridge University Press, 2000
13 M. Turk and A. Pentland, 'Eigenfaces for recognition,' Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991   DOI   ScienceOn