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동공의 반사특징을 이용한 얼굴위조판별 시스템

Fake Face Detection System Using Pupil Reflection

  • 양재준 (홍익대학교 전기정보제어공학과) ;
  • 조성원 (홍익대학교 전기정보제어공학과) ;
  • 정선태 (숭실대학교 정보통신전자공학부)
  • 투고 : 2010.08.04
  • 심사 : 2010.10.08
  • 발행 : 2010.10.25

초록

최근 지능형 범죄가 늘면서 첨단 보안 기술에 대한 요구가 점차 늘어나고 있다. 현재까지 보고된 위조영상검출방법은 실용화를 위하여 정확도 개선이 요구된다. 본 논문에서는 사람의 얼굴에 대하여 동공의 반사광을 이용한 얼굴위조판별 시스템을 제안한다. 제안된 시스템은 먼저 다중 스케일 가버특징 벡터를 기반으로 눈의 위치를 찾은 후 2단계의 템플릿 매칭을 통해서 설정된 적용범위를 벗어나는 눈에 대하여 위조판별을 고려하지 않음으로써 정확도를 높이는 방법을 사용한다. 신뢰도가 확보된 눈의 위치를 기반으로 적외선 조명에 반사되는 동공의 특징을 이용하여 눈위치 근처에서의 화소값을 계산하여 위조 여부를 판단한다. 실험을 통하여 본 논문에서 제안한 방법이 더욱 신뢰성 높은 위조판별시스템임을 확인하였다.

Recently the need for advanced security technologies are increasing as the occurrence of intelligent crime is growing fastly. Previous liveness detection methods are required for the improvement of accuracy in order to be put to practical use. In this paper, we propose a new fake image detection method using pupil reflection. The proposed system detects eyes based on multi-scale Gabor feature vector in the first stage, and uses template matching technique in oreder to increase the detection accuracy in the second stage. The template matching plays a role in determining the allowed eye area. The infrared image that is reflected in the pupil is used to decide whether or not the captured image is fake. Experimental results indicate that the proposed method is superior to the previous methods in the detection accuracy of fake images.

키워드

참고문헌

  1. J. Li, Y. Wang, T. Tan, and A. K. Jain, “Live face detection based on the analysis of fourier spectra,”In Biometric Technology for Human Identification, vol. 5404, pp. 296-303, 2004. https://doi.org/10.1117/12.541955
  2. S. Lin, P. Gang and Zhaohui Wu, "Blinking-Based Live Face Detection Using Conditional Random Fields", Springer, 2007
  3. 거리 정보를 이용한 위조 얼굴검출 방법 및 장치, 출원번호: 1020080131785, 발명자: 정성욱, 정윤수, 문기영
  4. O. Jesorsky, K. Kirchberg, and R. Frischholz, “Robust Face Detection Using the Hausdorff Distance,” In: J. Bigun, F. Smeraldi Eds. Lecture Notes in Computer Science 2091, Berlin: Springer, pp.90-95, 2001. https://doi.org/10.1007/3-540-45344-X_14
  5. H. Zhou and X. Geng, “Projection Functions for Eye Detection,” Pattern Recognition, No.5, pp.1049-1056, May 2004.
  6. Y. Ma, X. Ding, Z. Wang, and N. Wang, “Robust Precise Eye Location under Probabilistic Framework,” Proc. 6th IEEE Int’l Conf. on Automatic face and Gesture Recognition (FGR'04), pp.339-344, May 2004.
  7. P. Campadelli, R. Lanzarotti, and G. Lipori, “Precise eye localization through a general-tospecific model definition,” Proc. 17th conference organised by the British Machine Vision(BMVC 2006), 2006.
  8. Z. Niu, S. Shan, S. Yan, X. Chen, and W. Gao, “2D Cascaded Adaboost for Eye Localization,” 18th Int'l Conf. on Pattern Recognition, Vol.2, pp.1216-1219, Aug. 2006.
  9. R. Lienhart and J. Maydt, “An Extended Set of Haar-like Features for Rapid Object Detection,” IEEE ICIP 2002, Vol.1, pp.900-903, Sept. 2002.
  10. L. Wiskott, J. M. Fellous, N. Kuiger, C. von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol.19, pp. 775–779, July. 1997. https://doi.org/10.1109/34.598235
  11. D. V. Bolme, “Elastic Bunch Matching,” Master's Thesis, Colorado State University, 2003.
  12. 한국정보보호진흥원, $\ll$2006 국내 정보보호 산업통계조사 최종연구보고서$\gg$, 2006.
  13. S. Z. Li and A. K. Jain, “Handbook of Face Recognition,” Springer. 2004.
  14. P. Wang, M. B. Green, Q. Ji, and J. Wayman, “Automatic Eye Detection and Its Validation,” Computer Vision and Pattern Recognition, 2005 IEEE Computer Society Conference. III (June 2005), pp.164-172
  15. L.Wiskott, J.M Fellous, N. Kuiger, C. von der Malsburg, “Face Recognition by EBGM,” Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol.19, pp. 775-779, July.1997. https://doi.org/10.1109/34.598235
  16. R. Gross and V. Brajovic, “An image preprocessing algorithm for illumination invariant face recognition,” In Audio-and Video-Based Biometric Person Authentication, Vol.2688, pp.10-18, June. 2003. https://doi.org/10.1007/3-540-44887-X_2
  17. 김상훈, 정선태, “다중 해상도 가버 특징 벡터를 이용한 강인한 눈 검출,” 電子工學會論文誌 CI編 第45卷 第1號, 2008.1 : 1-96(88pages)
  18. A.Haro, I. Essa, and M. Flickner. “Detecting and tracking eyes by using their physiological properties,” In Proceedings of Conference on Computer Vision and Pattern Recognition, June 2000.

피인용 문헌

  1. Sleepiness Determination of Driver through the Frequency Analysis of the Eye Opening and Shutting vol.26, pp.6, 2016, https://doi.org/10.5391/JKIIS.2016.26.6.464