Face Recognition Technology and Its Real-World Application

  • Osamu Yamaguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7143)

Abstract

Facial image processing is a promising tool for consumer electronics and social infrastructure systems. In recent years, digital processing of a facial image can easily be performed with the spread of digital image apparatus, such as a digital camera and a mobile phone, by improvement of throughput of a computer. The performance of the face detection that is basic of the facial image processing improves drastically, and the computational cost has also decreased. It is the reason why that has expanded the application to various appliances. This paper introduces our group’s facial image processing algorithm as an example and trends of various applications using facial image processing in consumer electronics field and social infrastructure systems.

Keywords

Face Recognition Local Binary Pattern Face Detection Gesture Recognition Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kanade, T.: Picture Processing by Computer Complex and Recognition of Human Faces. doctoral dissertation, Kyoto University (1973)Google Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  3. 3.
    Wiskott, L., Fellous, J., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Patrern Analysis and Machine Intelligence 19(7), 775–779 (1997)CrossRefGoogle Scholar
  4. 4.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)CrossRefGoogle Scholar
  5. 5.
    Sung, K., Poggio, T.: Example-based learning for viewbased human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 39–51 (1998)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision (IJCV) 57(2), 137–154 (2004) (Originally appeared in CVPR 2001)CrossRefGoogle Scholar
  7. 7.
    Cootes, F.T., Taylor, J.C., Cooper, H.D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)CrossRefGoogle Scholar
  8. 8.
    Cootes, F.T., Edwards, J.G., Taylor, J.C.: Active appearance models. IEEE Transactions on Patrern Analysis and Machine Intelligence 23(6), 681–685 (2001)CrossRefGoogle Scholar
  9. 9.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Patrern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefMATHGoogle Scholar
  10. 10.
    Jain, A.K., Dass, S.C., Nandakumar, K.: Soft Biometric Traits for Personal Recognition Systems. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 731–738. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and Simile Classifiers for Face Verification. In: IEEE International Conference on Computer Vision (ICCV), pp. 365–372 (2009)Google Scholar
  12. 12.
    Mita, T., Kaneko, T., Stenger, B., Hori, O.: Discriminative Feature Co-Occurrence Selection for Object Detection. IEEE Transaction on Pattern Analysis Machine Intellgence 30(7), 1257–1269 (2008)CrossRefGoogle Scholar
  13. 13.
    Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 318–323 (1998)Google Scholar
  14. 14.
    Fukui, K., Yamaguchi, O.: Face Recognition using multi-viewpoint patterns for robot vision. In: 11th International Symposium of Robotics Research (ISRR 2003), pp. 192–201 (2003)Google Scholar
  15. 15.
    Nishiyama, M., Yamaguchi, O., Fukui, K.: Face Recognition with the Multiple Constrained Mutual Subspace Method. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 71–80. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Nishiyama, M., Yamaguchi, O.: Face Recognition Using the Classified Appearance-based Quotient Image. In: Proceedings Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG 2006), pp. 49–54 (2006)Google Scholar
  17. 17.
    Kozakaya, T., Yamaguchi, O.: Face Recognition by Projection-based 3D Normalization and Shading Subspace Orthogonalization. In: Proceedings Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG 2006), pp. 163–168 (2006)Google Scholar
  18. 18.
    Fukui, K., Stenger, B., Yamaguchi, O.: A Framework for 3D Object Recognition Using the Kernel Constrained Mutual Subspace Method. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 315–324. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Nishiyama, M., Yuasa, M., Shibata, T., Wakasugi, T., Kawahara, T., Yamaguchi, O.: Recognizing Faces of Moving People by Hierarchical Image-Set Matching. In: CVPR Workshop Biometrics 2007, pp. 1–8 (2007)Google Scholar
  20. 20.
    Yuasa, M., Kozakaya, T., Yamaguchi, O.: An Efficient 3D Geometrical Consistency Criterion for Detection of a Set of Facial Feature Points. In: Proceedings of the IAPR Conference on Machine Vision Applications (IAPR MVA 2007), pp. 25–28 (2007)Google Scholar
  21. 21.
    Kozakaya, T., Shibata, T., Yuasa, M., Yamaguchi, O.: Facial feature localization using weighted vector concentration approach. Image Vision Comput. (IVC) 28(5), 772–780 (2010)CrossRefGoogle Scholar
  22. 22.
    Maeda, K.: From the Subspace Methods to the Mutual Subspace Method. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds.) Computer Vision. SCI, vol. 285, pp. 135–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    Nishiyama, M., Hadid, A., Takeshima, H., Shotton, J., Kozakaya, T., Yamaguchi, O.: Facial Deblur Inference using Subspace Analysis for Recognition of Blurred Faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(4), 838–845 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Osamu Yamaguchi
    • 1
  1. 1.Power and Industrial Systems R&D CenterTOSHIBA Corporation Power System CompanyTokyoJapan

Personalised recommendations