Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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Face Recognition Method by Using Infrared and Depth Images

적외선과 깊이 영상을 이용한 얼굴 인식 방법

  • 이동석 (동의대학교 컴퓨터소프트웨어공학과) ;
  • 한대현 (동의대학교 전자공학과) ;
  • 권순각 (동의대학교 컴퓨터소프트웨어공학과)
  • Received : 2018.03.09
  • Accepted : 2018.03.26
  • Published : 2018.04.30
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Abstract

In this paper, we propose a face recognition method which is not sensitive to illumination change and prevents false recognition of photographs. The proposed method uses infrared and depth images at the same time, solves sensitivity of illumination change by infrared image, and prevents false recognition of two - dimensional image such as photograph by depth image. Face detection method using infrared and depth images simultaneously and feature extraction and matching method for face recognition are realized. Simulation results show that accuracy of face recognition is increased compared to conventional methods.

본 논문은 조명변화에 민감하지 않고, 사진에 대한 오인식을 방지하기 위한 얼굴인식 방법을 제안한다. 제안한 방법은 적외선과 깊이 영상을 동시에 이용하며, 적외선 영상으로 조명변화의 민감성을 해결하고, 깊이 영상으로 사진과 같은 2차원 영상에 대한 오인식을 방지한다. 적외선과 깊이 영상을 동시에 이용한 얼굴 검출 방법과 얼굴 인식을 위한 특징 추출 및 매칭 방법을 구현하였으며, 모의실험을 통하여 기존 방법에 비해 얼굴인식의 정확도가 증가함을 보인다.

Keywords

References

  1. Turk, M. A., and Pentland, A. P., "Face Recognition Using Eigenfaces," Proceeding of IEEE Computer Society Conference on Computer Vision and pattern Recognition, 1991, pp. 586-591.
  2. Paul, L. C., and Sumam, A. A., "Face Recognition Using Principal Component Analysis Method," International Journal of Advanced Research in Computer Engineering & Technology, Vol. 1, No. 9, 2012, pp. 135-139.
  3. Lowe, D. G., "Object Recognition from Local Scale-Invariant Features," Proceeding of the International Conference on Computer Vision, Vol. 2, 1999, pp. 1150-1157.
  4. Geng, C. and Jiang, X., "Face Recognition Using Sift Features," Proceeding of IEEE International Conference on Image Processing, 2009, pp. 3313-3316.
  5. Viola, P. and Jones, M. J., "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. 511-518.
  6. Ojala, T., Pietikäinen, M., and Harwood, D., "A Comparative Study of Texture Measures with Classification Based on Feature Distributions," Pattern Recognition, Vol. 29, No. 1, 1996, pp. 51-59. https://doi.org/10.1016/0031-3203(95)00067-4
  7. Ahonen, T., Hadid, A., and Pietikinen, M., "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, 2006, pp. 2037-2041. https://doi.org/10.1109/TPAMI.2006.244
  8. Liao, S., Zhu, X., Lei, Z., Zhang, L., and Li, S. Z., "Learning Multi-scale Block Local Binary Patterns for Face Recognition," Proceeding of International Conference on Biometrics, 2007, pp. 828-837.
  9. Pietikainen, M., Hadid, A., Zhao, G., and Ahonen, T., Computer Vision Using Local Binary Patterns, Springer, Berlin, Germany, 2011.
  10. Lahdenoja, O., Poikonen, J., and Laiho, M., "Towards Understanding The Formation of Uniform Local Binary Patterns," ISRN Machine Vision, 2013, pp. 1-20.
  11. Kim, S. M., Lee, D. J., Song, C. K., and Chun, M. G., "Robust Face Recognition Against Illumination Change Using Visible and Infrared Images," Journal of Korean Institute of Intelligent Systems, Vol. 24, No. 4, 2014, pp. 343-348. https://doi.org/10.5391/JKIIS.2014.24.4.343
  12. Kong, S. G., "Design of an Observer-based Decentralized Fuzzy Controller for Discrete-Time Interconnected Fuzzy Systems," Journal of Korean Institute of Intelligent Systems, Vol. 25, No. 5, 2015, pp. 437-443. https://doi.org/10.5391/JKIIS.2015.25.5.437
  13. Kwon, K. H., "Evaluation of Depth Image of IR Range Sensor with Face Recognition Algorithms," Journal of The Korea Academia-Industrial Cooperation Society, Vol. 13, No. 8, pp. 3666-3671, 2012. https://doi.org/10.5762/KAIS.2012.13.8.3666
  14. Kwon, S. K., Kim, H. J., and Lee, D. S., "Face Recognition Method Based on Local Binary Pattern Using Depth Images," Journal of the Korea Industrial Information Systems Research, Vol. 22, No. 6, 2017, pp. 39-45. https://doi.org/10.9723/JKSIIS.2017.22.6.039
  15. Ojala, T., Pietikainen, M., and Maenpaa, M., "Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, 2002, pp. 971-987. https://doi.org/10.1109/TPAMI.2002.1017623
  16. Huang, Y., Wang, Y., and Tan, T., "Combining Statistics of Geometrical and Correlative Features for 3D Face Recognition," Proceeding of the British Machine Vision Conference, 2006, pp. 90.1-90.10.
  17. Neto, J. B. C., and Marana. A. N., "3DLBP and HAOG Fusion for Face Recognition Utilizing Kinect as a 3D Scanner," Proceeding of the 30th Annual ACM Symposium on Applied Computing. ACM, 2015, pp. 66-73.
  18. 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, 2017, pp. 43-50. https://doi.org/10.9717/JMIS.2017.4.1.43
  19. Viola, P. and Jones, M., "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proceeding of the 2001 IEEE Computer Society Conference on, Vol. 1, 2001, pp. 511-518.

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