ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Tiny and Blurred Face Alignment for Long Distance Face Recognition

  • Ban, Kyu-Dae (IT Convergence Technology Research Laboratory, ETRI, Department of Computer Software & Engineering, University of Science and Technology) ;
  • Lee, Jae-Yeon (IT Convergence Technology Research Laboratory, ETRI) ;
  • Kim, Do-Hyung (IT Convergence Technology Research Laboratory, ETRI) ;
  • Kim, Jae-Hong (IT Convergence Technology Research Laboratory, ETRI) ;
  • Chung, Yun-Koo (IT Convergence Technology Research Laboratory, ETRI, Department of Computer Software & Engineering, University of Science and Technology)
  • Received : 2010.02.16
  • Accepted : 2010.09.30
  • Published : 2011.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Applying face alignment after face detection exerts a heavy influence on face recognition. Many researchers have recently investigated face alignment using databases collected from images taken at close distances and with low magnification. However, in the cases of home-service robots, captured images generally are of low resolution and low quality. Therefore, previous face alignment research, such as eye detection, is not appropriate for robot environments. The main purpose of this paper is to provide a new and effective approach in the alignment of small and blurred faces. We propose a face alignment method using the confidence value of Real-AdaBoost with a modified census transform feature. We also evaluate the face recognition system to compare the proposed face alignment module with those of other systems. Experimental results show that the proposed method has a high recognition rate, higher than face alignment methods using a manually-marked eye position.

Keywords

References

  1. L. Aryananda, "Recognizing and Remembering Individuals: Online and Unsupervised Face Recognition for Humanoid Robot," IEEE/RSJ Int. Conf. Intell. Robots Syst., Sept. 2002, pp. 1202-1207.
  2. M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cog. Neuroscience, vol. 3, no. 1, 1991, pp. 71-86. https://doi.org/10.1162/jocn.1991.3.1.71
  3. D. Hansen and Q. Ji, "In the Eye of the Beholder: A Survey of Models for Eyes and Gaze," IEEE Trans. Pattern Anal. Mach. Intell. (submitted).
  4. H. Kim and W. Kim, "Eye Detection in Facial Images Using Zernike Moments with SVM," ETRI J., vol. 30, no. 2, Apr. 2008, pp. 335-337. https://doi.org/10.4218/etrij.08.0207.0150
  5. M. Zhao and T. Chua, "Markovian Mixture Face Recognition with Discriminative Face Alignment," IEEE Int. Conf. Autom. Face Gesture Recog., Sept. 2008, pp. 1-6.
  6. L. Wang, X. Ding, and C. Fang, "Generic Face Alignment using an Improved Active Shape Model," Int. Conf. Audio, Language, Image Process., July 2008, pp. 317-321
  7. F. Kahraman and M. Gokmen, "Illumination Invariant Three-Stage Approach for Face Alignment," IEEE Intl. Conf. Image Process., Oct. 2006, pp. 2073-2076.
  8. Y. Su, H. Ai, and S. Lao, "Real-Time Face Alignment with Tracking in Video," IEEE Int. Conf. Image Process., Oct. 2008, pp. 1632-1635.
  9. Y. Zhou et al., "A Bayesian Mixture Model for Multi-View Face Alignment," IEEE Computer Society Conf. Computer Vision Pattern Recog., vol. 2, June 2005, pp. 741-746.
  10. K. Choi, J. Ahn, and H. Byun, "Face Alignment Using Segmentation and a Combined AAM in a PTZ Camera," Int. Conf. Pattern Recog., vol. 3, Sept. 2006, pp. 1191-1194.
  11. X. Liu, "Generic Face Alignment Using Boosted Appearance Model," IEEE Conf. Computer Vision Pattern Recog., June 2007, pp. 1-8.
  12. Y. Huang et al., "Face Alignment under Variable Illumination," IEEE Int. Conf. Autom. Face Gesture Recog., May 2004, pp. 85-90.
  13. P. Wang, L. Tran, and Q. Ji, "Improving Face Recognition by Online Image Alignment," Int. Conf. Pattern Recog., vol. 1, Sept. 2006, pp. 311-314.
  14. F. Kahraman, B. Kurt, and M. Gokmen, "Robust Face Alignment for Illumination and Pose Invariant Face Recognition," IEEE Conf. Computer Vision Pattern Recog., June 2007, pp. 1-7.
  15. P. Wang et al., "Automatic Eye Detection and its Validation," IEEE Computer Soc. Conf. Computer Vision Pattern Recog., June 2005, pp. 164-164.
  16. T. F. Cootes, G. J. Edwards, and C. J. Taylor, "Active Appearance Models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, June 2001, pp. 681-685. https://doi.org/10.1109/34.927467
  17. Y. Freund and R.E. Shapire, "A Short Introduction to Boosting," J. Japanese Society for Art. Intell., no. 14, Sept. 1999, pp. 771-780.
  18. J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression: a Statistical View of Boosting," Annals of Statistics, vol. 38, no. 2, Apr. 2000, pp. 337-374.
  19. B. Froba and A. Ernst, "Face Detection with the Modified Census Transform," Int. Conf. Autom. Face Gesture Recog., 2004, pp. 91-96.
  20. B. Jun and D. Kim, "Robust Real-Time Face Detection Using Face Certainty Map," Intl. Conf. Biometrics, vol. 4642, no. 125, Aug. 2007, pp. 29-38.
  21. P. Viola and M. Jones, "Robust Real Time Object Detection," IEEE ICCV Workshop Statistical Computational Theories of Vision, July 2001, pp. 1-25.
  22. M. Castrillon Santana et al., "Face and Face Feature Detection Evaluation - Performance Evaluation of Public Domain Haar Detectors for Face and Face Feature Detection," VISAPP. vol. 2, 2008, pp. 167-172.
  23. Ho-Sub Yoon et al., "Face Component Detection on the Natural Office Scene," Intl. Conf. Adv. Intell. Mechatronics, 1997, p. 28.
  24. D. Kim et al., "A Non-Cooperative User Authentication System in Robot Environments," IEEE Trans. Consum. Electron., vol. 53, no. 2, May 2007, pp. 804-811. https://doi.org/10.1109/TCE.2007.381763
  25. Intel. Open Source Computer Vision Library (OpenCV). Available at: http://sourceforge.net/projects/opencvlibrary/

Cited by

  1. 뉴턴 최적화를 통해 개선된 아다부스트 훈련과 MCT 특징을 이용한 번호판 검출 vol.17, pp.12, 2011, https://doi.org/10.9708/jksci/2012.17.12.071
  2. Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations vol.2013, pp.None, 2013, https://doi.org/10.1155/2013/153465
  3. Robust Sign Recognition System at Subway Stations Using Verification Knowledge vol.36, pp.5, 2011, https://doi.org/10.4218/etrij.14.2214.0007
  4. 거리별 얼굴영상 자동 생성 방법을 이용한 원거리 얼굴인식 시스템 vol.51, pp.11, 2011, https://doi.org/10.5573/ieie.2014.51.11.137
  5. 줌 카메라를 통해 획득된 거리별 얼굴 영상을 이용한 원거리 얼굴 인식 기술 vol.24, pp.6, 2011, https://doi.org/10.13089/jkiisc.2014.24.6.1139
  6. Uncooperative Person Recognition Based on Stochastic Information Updates and Environment Estimators vol.37, pp.2, 2015, https://doi.org/10.4218/etrij.15.0114.0037
  7. Real-Time License Plate Detection in High-Resolution Videos Using Fastest Available Cascade Classifier and Core Patterns vol.37, pp.2, 2011, https://doi.org/10.4218/etrij.15.2314.0077