포즈 변화에 강인한 얼굴 인식

Face Recognition Robust to Pose Variations

  • 노진우 (고려대학교 전자컴퓨터공학과) ;
  • 문인혁 (재활공학연구소) ;
  • 고한석 (고려대학교 전자컴퓨터공학과)
  • 발행 : 2004.09.01

초록

본 논문에서는 포즈 변화에 강인한 얼굴 인식을 위하여 원통 모델을 이용하는 방법을 제안한다. 얼굴 모양이 원통형이라는 가정 하에 입력 영상으로부터 대상의 포즈를 예측하고, 예측된 포즈 각도만큼 포즈 변환을 실시하여 정면 얼굴 영상을 획득한다. 이렇게 획득한 정면 영상을 얼굴 인식에 적용함으로써 얼굴 인식률을 향상시킬 수 있다. 실험 결과, 포즈 변환을 통하여 인식률이 61.43%에서 93.76%로 향상되었음을 볼 수 있었으며, 보다 복잡한 3차원 얼굴 모델을 이용한 결과와 비교하였을 때 비교적 양호한 인식률을 갖는 것을 확인하였다.

This paper proposes a novel method for achieving pose-invariant face recognition using cylindrical model. On the assumption that a face is shaped like that of a cylinder, we estimate the object's pose and then extract the frontal face image via a pose transform with previously estimated pose angle. By employing the proposed pose transform technique we can increase the face recognition performance using the frontal face images. Through representative experiments, we achieved an increased recognition rate from 61.43% to 94.76% by the pose transform. Additionally, the recognition rate with the proposed method achieves as good as that of the more complicated 3D face model.

키워드

참고문헌

  1. 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
  2. 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 https://doi.org/10.1109/CVPR.1994.323814
  3. 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 https://doi.org/10.1109/AFGR.2000.840642
  4. T. S. Jebara, '3D Pose estimation and normalization for face recognition,' McGill University, 1996
  5. S. Akamatsu, T. Sasaki, H. Fukumachi, and Y. Suenaga, 'A robust face identification scheme - KL expansion of an invariant feature space,' SPIE Proc., vol. 1607, pp. 71-84, Nov 1991 https://doi.org/10.1117/12.57048
  6. 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 https://doi.org/10.1109/AFGR.1998.670973
  7. 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 https://doi.org/10.1109/34.41390
  8. M. Turk and A. Pentland, 'Eigenfaces for recognition,' Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991 https://doi.org/10.1162/jocn.1991.3.1.71
  9. M.-H. Yang, D. J. 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 https://doi.org/10.1109/34.982883