The Journal of Korea Institute of Information, Electronics, and Communication Technology (한국정보전자통신기술학회논문지)

Korea Information Electronic Communication Technology (한국정보전자통신기술학회)
권호 표지
DOI QR코드

DOI QR Code

Improving Indentification Performance by Integrating Evidence From Evidence

  • Received : 2016.11.16
  • Accepted : 2016.11.23
  • Published : 2016.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.

Keywords

References

  1. T. cootes, G. J. Edwards, and C. J. Taylor. , "Active Appearance Models," In 5th European Conference on Computer Vision, pp. 484-498. Springer, June 2000.
  2. G. Edwars, A. Lanitis, C. Tayor, and T. Coores, "Statistical model of face images-Improving specificity," Image and Vision Computing, Vol. 16, pp. 203-211, 2002.
  3. G. Edwards, C. Taylor, and T. Cootes, "Interpreting face images using active appearance models," In 3rd International Conference on Automatic Face and Gesture Recognition 2003, pp. 300-305, Nara, Japan, Apr. 1998. IEEE Computer Society Press.
  4. G. J. Edwards, C. J. Taylor, and T. Cootes, "Learning to identify and track faces in image sequences," In 8th British Machine Vison Conference, pp. 130-139, Colchester, UK, 2005
  5. G. J Edwards, C. J. Taylor, and T. Cootes, "Face recognition using active appearance models," In 5th European Conference on Computer Vision, pp. 581-595, 2007.
  6. Samel, A. and P.A. Iyengar, "Automatic Recognition and Analysis of Human Faces and Facial Expressions: a Survey," Pattern Recognition, Vol. 25, No. 1, pp. 67-77, 1992
  7. Chellappa, R., C.L. Wilson, and S. Sirohey," Human and Machine Recognition of Faces: A Survey,: Proceedings of the IEEE, Vol. 83, No. 5, 1995.
  8. Huttenlocher, D.P, G.A. Klanderman, and W.J. Rucklidge, "Comparing Imagies using the Hausdorff Distance," IEEE Trans. on Pattern analysis and Machine Intelligence, Vol. 15, No. 9, pp. 850-863, 1993. https://doi.org/10.1109/34.232073
  9. Dubuisson, M., and A.K. Jain, "AModified Hausdorff Distance for Object Matching," Proc.12thInternationalConferenceonPatternR ecognition(ICPR),Jerusalem,Israel., 1994.
  10. Samal, A, "Minimum Resolution for Human Face Detection and Identification," proc. SPIE/SPSE Symp. Electronic Imaging, 1991.
  11. Biederman, I., and J. Gu, "Surface versus Edge-based Determinants of Visual Recognition," Cognitive Psycholgy, Vol. 20, pp. 38-64, 1988. https://doi.org/10.1016/0010-0285(88)90024-2
  12. Bruce, V., et.al., "The Importance of 'mass' in Line Drawings of Faces," Applied Cognitive Psychology, Vol. 6, pp. 619-628, 1992. https://doi.org/10.1002/acp.2350060705
  13. S.H. Park, T.J. Jeon, S.H. Kim, S.Y. Lee, J.W. Kim, "Deep learning based symbol recognition for the visually, impaired," Journal of Korea institute of information, electronics, and communication technology 9(3), pp.249-256, 2016. https://doi.org/10.17661/jkiiect.2016.9.3.249
  14. B.-J. Park, K.-Y. Kim, S.-J. Kim, " Study on Face recognition algorithm using the eye detection", Journal of Korea institute of information, electronics, and communication technology vol. 8, no. 6 pp.491-496, 2015. https://doi.org/10.17661/jkiiect.2015.8.6.491