깊이 영상을 이용한 지역 이진 패턴 기반의 얼굴인식 방법

Face Recognition Method Based on Local Binary Pattern using Depth Images

  • 권순각 (동의대학교 컴퓨터소프트웨어공학과) ;
  • 김흥준 (동의대학교 컴퓨터소프트웨어공학과) ;
  • 이동석 (동의대학교 컴퓨터소프트웨어공학과)
  • 투고 : 2017.12.06
  • 심사 : 2017.12.28
  • 발행 : 2017.12.31


기존의 색상기반 얼굴인식 방법은 조명변화에 민감하며, 위변조의 가능성이 있기 때문에 다양한 산업분야에 적용되기 어려운 문제가 있었다. 본 논문에서는 이러한 문제를 해결하기 위해 깊이 영상을 이용한 지역 이진 패턴(LBP) 기반의 얼굴인식 방법을 제안한다. 깊이 정보를 이용한 얼굴 검출 방법과 얼굴 인식을 위한 특징 추출 및 매칭 방법을 구현하고, 모의실험 결과를 바탕으로 제안된 방식의 인식 성능을 나타낸다.

Conventional Color-Based Face Recognition Methods are Sensitive to Illumination Changes, and there are the Possibilities of Forgery and Falsification so that it is Difficult to Apply to Various Industrial Fields. In This Paper, we propose a Face Recognition Method Based on LBP(Local Binary Pattern) using the Depth Images to Solve This Problem. Face Detection Method Using Depth Information and Feature Extraction and Matching Methods for Face Recognition are implemented, the Simulation Results show the Recognition Performance of the Proposed Method.



연구 과제 주관 기관 : 한국연구재단


  1. Boutellaa, E, et al., "On the use of Kinect Depth Data for Identity, Gender and Ethnicity Classification from Facial Images," Pattern Recognition Letters 68, pp. 270-277. 2015.
  2. Shin, D. W., Park, S. J., and Ko, J. P., “ 3D Face Alignment and Normalization Based on Feature Detection Using Active Shape Models : Quantitative Analysis on Aligning Process,” Korean Journal of Computational Design and Engineering, Vol. 13, No. 6, pp. 403-411, 2008.
  3. Viola, P., and Jones, M., "Rapid Object Detection using a Boosted Cascade of Simple Features," In Computer Vision and Pattern Recognition, CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on IEEE, Vol. 1. pp. I-I, 2001.
  4. Ojala, T., Pietikainen, M., and Maenpaa, T., “Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971-987, 2002.
  5. Ahonen, T., Hadid, A., and Pietikainen, M., "Face Recognition with Local Binary Patterns," Proc. European Conf. on Computer Vision, pp. 469-481, 2004.
  6. Rodriguez, Y., and Marcel, S., "Face Authentication using Adapted Local Binary Pattern Histograms," Proc. European Conf. on Computer Vision, pp. 321-332, 2006.
  7. Shan, C., and Gritti, T., "Learning Discriminative LBP-histogram Bins for Facial Expression Recognition," Proc. British Machine Vision Conf., 2008.
  8. Ahonen, T., Hadid, A., and Pietikainen, M., “Face Description with Local Binary Patterns : Application to Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037-2041, 2006.
  9. Kim, H. J., Lee, D. S. and Kwon, S. K., “Implementation of Nose and Face Detections in Depth Image,” Journal of Multimedia Information System, Vol. 4, No. 1, pp. 43-50, 2017.
  10. Cardia Neto, J. B., and Marana, A. N., "3DLBP and HAOG Fusion for Face Recognition Utilizing Kinect as a 3D Scanner. In Proceedings of the 30th Annual ACM Symposium on Applied Computing. ACM, pp. 66-73, 2015.
  11. Bayramoglu, N., Zhao, G., and Pietikäinen, M. "CS-3DLBP and Geometry Based Person Independent 3D Facial Action Unit Detection," In Biometrics(ICB), 2013 International Conference on IEEE, pp. 1-6, 2013.
  12. Rolle, A., et al., “Effects of Human Cytomegalovirus Infection on Ligands for the Activating NKG2D Receptor of NK Cells: Up-regulation of UL16-binding Protein (ULBP) 1 and ULBP2 is Counteracted by the Viral UL16 Protein,” The Journal of Immunology, Vol. 171, No. 2, pp. 902-908, 2003.
  13. Sanchez Lopez, L., "Local Binary Patterns Applied to Face Detection and Recognition," Research Report, 2010.