Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Face Detection Using Adaboost and Template Matching of Depth Map based Block Rank Patterns

Adaboost와 깊이 맵 기반의 블록 순위 패턴의 템플릿 매칭을 이용한 얼굴검출

  • Kim, Young-Gon (Department of Electronic Engineering, School of Engineering, Sogang University) ;
  • Park, Rae-Hong (Department of Electronic Engineering, School of Engineering, Sogang University) ;
  • Mun, Seong-Su (Department of Electronic Engineering, School of Engineering, Sogang University)
  • Received : 2012.03.15
  • Accepted : 2012.04.27
  • Published : 2012.05.30
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Abstract

A face detection algorithms using two-dimensional (2-D) intensity or color images have been studied for decades. Recently, with the development of low-cost range sensor, three-dimensional (3-D) information (i.e., depth image that represents the distance between a camera and objects) can be easily used to reliably extract facial features. Most people have a similar pattern of 3-D facial structure. This paper proposes a face detection method using intensity and depth images. At first, adaboost algorithm using intensity image classifies face and nonface candidate regions. Each candidate region is divided into $5{\times}5$ blocks and depth values are averaged in each block. Then, $5{\times}5$ block rank pattern is constructed by sorting block averages of depth values. Finally, candidate regions are classified as face and nonface regions by matching the constructed depth map based block rank patterns and a template pattern that is generated from training data set. For template matching, the $5{\times}5$ template block rank pattern is prior constructed by averaging block ranks using training data set. The proposed algorithm is tested on real images obtained by Kinect range sensor. Experimental results show that the proposed algorithm effectively eliminates most false positives with true positives well preserved.

흑백 혹은 컬러 영상과 같은 2차원 정보를 사용한 얼굴 검출 알고리즘에 관한 연구가 수십 년 동안 이루어져 왔다. 최근에는 저가 range 센서가 개발되어, 이를 통해 3차원 정보 (깊이 정보: 카메라와 물체사이의 거리를 나타냄)를 손쉽게 이용함으로써 얼굴의 특징을 높은 신뢰도로 추출하는 것이 가능해졌다. 대부분 사람 얼굴에는 3차원적인 얼굴의 구조적인 특징이 있다. 본 논문에서는 흑백 영상과 깊이 영상을 사용하여 얼굴을 검출하는 알고리즘을 제안한다. 처음에는 흑백 영상에 adaboost를 적용하여 얼굴 후보 영역을 검출한다. 얼굴 후보 영역의 위치에 대응되는 깊이 영상에서의 얼굴 후보 영역을 추출한다. 추출된 영역의 크기를 $5{\times}5$ 영역으로 분할하여 깊이 값의 평균값을 구한다. 깊이 값들의 평균값들 간에 순위를 매김으로써 블록 순위 패턴이 생성된다. 얼굴 후보 영역의 블록 순위 패턴과 학습 데이터를 사용하여 미리 학습된 템플릿 패턴을 매칭함으로써 최종 얼굴 영역인지 아닌지를 판단할 수 있다. 제안하는 방법의 성능을 Kinect sensor로 취득한 실제 영상으로 실험하였다. 실험 결과 true positive를 잘 보존하면서 많은 false positive들을 효과적으로 제거하는 것을 보여준다.

Keywords

References

  1. M.-H. Yang, D. J. Kriegman, and N. Ahuja, "Detecting faces in images: A survey," IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 1, pp. 34-58, Jan. 2002. https://doi.org/10.1109/34.982883
  2. P. Viola and M. Jones, "Robust real-time face detection," Int. J. Computer Vision, vol. 57, no. 2, pp. 137-154, May 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  3. H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based on face detection," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 1, pp. 22-38, Jan. 1998.
  4. L.-L. Huang, A. Shimizu, and H. Kobatake, "Robust face detection using Gabor filter features," Pattern Recognition Letters, vol. 26, no. 11, pp. 1641-1649, Aug. 2005. https://doi.org/10.1016/j.patrec.2005.01.015
  5. K.-K. Sung and T. Poggio, "Example-based learning for view-based human face detection," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 1, pp. 39-51, Jan. 1998. https://doi.org/10.1109/34.655648
  6. R. Brunelli and T. Poggio, "Face recognition: Features versus templates," IEEE Trans. Pattern Anal. Machine Intell., vol. 15, no. 10, pp. 1042-1052, Feb. 1993. https://doi.org/10.1109/34.254061
  7. A. Yuille, P. Hallinan, and D. Cohen, "Feature extraction from faces using deformable templates," Int. J. Computer Vision, vol. 8, no. 2, pp. 99-111, Nov. 1992. https://doi.org/10.1007/BF00127169
  8. S. Kherchaoui and A. Houacine, "Face detection based on a model of the skin color with constraints and template matching," in Proc. Int. Conf. Machine and Web Intelligence, Algiers, Algeria, pp. 469-472, Oct. 2010.
  9. Y. Hori, K. Shimizu, Y. Nakamura, and T. Kuroda, "A real-time multi face detection technique using positive-negative lines-of-face template," in Proc. Int. Conf. Pattern Recognition, vol. 1, pp. 765-768, Cambridge, UK, Aug. 2004.
  10. H. Liu, S. Yan, X. Chen, and W. Gao, "Rotated face detection in color images using radial template (RT)," in Proc. Int. Conf. Acoustics, Speech, and Signal Processing, vol. 3, pp. 213-216, Hong Kong, China, Apr. 2003.
  11. S.-H. Jeng, H.-Y. M. Liao, C.-C. Han, M.-Y. Chern, and Y. T. Liu, "Facial feature detection using geometrical face model: An efficient approach," Pattern Recognition, vol. 31, no. 3, pp. 273-282, Mar. 1998. https://doi.org/10.1016/S0031-3203(97)00048-4
  12. P. S. Hiremath and A. Danti, "Detection of multiple faces in an image using skin color information and lines-of-separability face model," Int. J. Pattern Recognition and Artificial Intell., vol. 20, no. 1, pp. 39-61, Jan. 2006. https://doi.org/10.1142/S021800140600451X
  13. J.-W. Wang, "Precise face segmentation for recognition," in Proc. Int. Conf. Image Processing, pp. 2045-2048, Atlanta, GA, USA, Oct. 2006.
  14. T. Ahonen, A. Hadid, and M. Pietikäinen, "Face description with local binary patterns: Application to face recognition," IEEE Trans. Pattern Anal. Machine Intell, vol. 28, no. 12, pp. 2037-2041, Dec. 2006. https://doi.org/10.1109/TPAMI.2006.244
  15. M. Nilsson, J. Nordberg, and I. Claesson, "Face detection using local SMQT features and split up snow classifier," in Proc. IEEE Int. Conf.Acoustics, Speech, and Signal Processing, vol. 2, pp. 589-592, Honolulu, HI, USA, Apr. 2006.
  16. S. Kosov, K. Scherbaum, K. Faber, T. Thormahlen, and H.-P. Seidel, "Rapid stereo vision enhanced face detection," in Proc. Int. Conf. Image Processing, vol. 4, pp. 1221-1224, Cairo, Egypt, Nov. 2009.
  17. H. Wu, K. Suzuki, T. Wada, and Q. Chen, "Accelerating face detection by using depth information," Advances in Image and Video Technology, Third Pacific Rim Symposium, PSIVT 2009, Lecture Notes in Computer Science, pp. 657−667, Tokyo, Japan, Jan. 2009.
  18. F. Jan, S. Daniel, and V. Alexander, "Face detection using 3D time of flight and colour cameras," in Proc. 41st Int. Symposium and 6th German Conf. Robotics, pp. 112−116, Frankfurt, Germany, June 2010.
  19. J. Qian, S. Ma, Z. Hao, and Y. Shen, "Face detection and recognition method based on skin color and depth information," in Proc. Consumer Electronics, Communications and Networks, pp. 345−348, XianNing, China, Apr. 2011.
  20. Y.-G. Kim and R.-H. Park, "Face detection using adaboost and template matching of depth map based $3{\times}3$ block rank pattern," in Proc. The 2012 Int. Workshop Advanced Image Technology, p. 51 (51:1-6), Ho Chi Minh City, Vietnam, Jan. 2012.
  21. http://www.xbox.com/en-US/kinect.
  22. K.-S. Park, R.-H. Park, and Y.-G. Kim, "Face detection using the $3{\times}3$ block rank patterns of gradient magnitude images and a geometric face model," in 2011 Digest of Technical Papers Int. Conf. Consumer Electronics, pp. 818−819, Las Vegas, NV, USA, Jan. 2011.
  23. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, Sebastopol, CA, USA: O'Reilly Media Inc., 2008.
  24. T. C. W. Landgrebe, P. Paclik, R. P. W. Duin, and A. P. Bradley, "Precision-recall operating characteristic (P-ROC) curves in imprecise environments," in Proc. Int. Conf. Pattern Recognition, vol. 4, pp. 123-127, Hong Kong, China, Aug. 2006.