CFP last date
22 April 2024
Reseach Article

Measurement based Recognition of Human Faces under Varying Poses

Published on November 2011 by K. R. Singh, Bakul Pandhre, Roshni Khedgaonkar
2nd National Conference on Information and Communication Technology
Foundation of Computer Science USA
NCICT - Number 2
November 2011
Authors: K. R. Singh, Bakul Pandhre, Roshni Khedgaonkar
325acb95-6f1e-44b3-99ca-4e6ea2a4818f

K. R. Singh, Bakul Pandhre, Roshni Khedgaonkar . Measurement based Recognition of Human Faces under Varying Poses. 2nd National Conference on Information and Communication Technology. NCICT, 2 (November 2011), 21-25.

@article{
author = { K. R. Singh, Bakul Pandhre, Roshni Khedgaonkar },
title = { Measurement based Recognition of Human Faces under Varying Poses },
journal = { 2nd National Conference on Information and Communication Technology },
issue_date = { November 2011 },
volume = { NCICT },
number = { 2 },
month = { November },
year = { 2011 },
issn = 0975-8887,
pages = { 21-25 },
numpages = 5,
url = { /proceedings/ncict/number2/4287-ncict014/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd National Conference on Information and Communication Technology
%A K. R. Singh
%A Bakul Pandhre
%A Roshni Khedgaonkar
%T Measurement based Recognition of Human Faces under Varying Poses
%J 2nd National Conference on Information and Communication Technology
%@ 0975-8887
%V NCICT
%N 2
%P 21-25
%D 2011
%I International Journal of Computer Applications
Abstract

Till date many approaches have been proposed for recognizing the human faces under the different orientations. In this paper we propose a new measurement based approach to measure all the small measurements that can enable us to identify a person uniquely. Here we consider 4 facial photographs taken from correct positions, which provide us with the required information. The idea is to make the approach highly analytical which has little or no effect of variable factors like illumination, pose etc. With the presented idea of Most Informative Photograph (MIP), we consider a number of facial photographs 4 in our case and find the average of the values for comparison with the database. This in turn shall improve the efficiency by a greater amount.

References
  1. Y. Adini, Y. Moses, S. Ullman; “Face recognition: the problem of compensating for changes illumination direction”,IEEE Transactions on Pattern Analysis and Machine, vol.19, pp.721-732, 1997.
  2. W.Y. Zhao, R. Chellappa; “Illumination-insensitive face recognition using symmetric shape-from-shading”, IEEE Conference on Computer Vision and Pattern Recognition, vol.1, pp. 1286, 2000.
  3. P.N Belhumeur, J.P Hespanha, D.J Kriegman; “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection”, IEEE Transactions on pattern analysis and machine, vol.19, pp.711-720, 1997.
  4. B. M. Stewart, T. Sejnowski; “Viewpoint invariant face recognition using independent Component analysis and attractor networks”, In M. Mozer, M. Jordan, & T. Petsche, (Eds.),Advances in Neural Information Processing Systems 9. Cambridge, MA: MIT Press: pp. 817-823, 1997.
  5. P.N Belhumeur, D.J Kriegman; “What is the set of images of an object under all possible illumination conditions”, Int. J. Computer Vision, vol. 28, no.3, pp.245–260, 1998.
  6. O. Arandjelovic, R. Cipolla; “A Methodology for rapid illumination-Invariant face recognition using image processing filters”, Int. J. Computer Vision and Image Understanding, pp. 159-171, 2009.
  7. T. Zhang, Y. Y. Tang, Z. Shang, X. Liu; “Face Recognition Under Varying Illumination using Gradientfaces”, IEEE Transactions, vol.18, no.11, pp.2599 – 2606, 2009.
  8. M. Lades, J. C. Vorbruggen, J. Buhmann, J.Lange, C. Malsburg, R. P. Wurtz, W. Konen, “Distortation invariant object recognition in the dynamic link architecture”, IEEE Trans. , ,vol.42, pp.301-311,1993.
  9. L. Nanni, D. Maio, “Weighted sub-Gabor for face recognition”, Int. J. Pattern Recognition Letters, vol.28, no.4, pp. 487-492, 2007.
  10. A. Georghiades, D. Kriegman, P. Belhumeur; “Illumination cones for recognition under variable lighting: Faces”, In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pp. 52–59, 1998.
  11. P. Hallinan; “A Deformable Model for Face Recognition under Arbitrary Lighting Conditions”, PhD thesis, Harvard University, 1995.
  12. P. Hallinan; G. Gordon, A. Yuille, D. Mumford, “Two- and Three-Dimensional Patterns of the Face”, A.K.Peters, 1999.
  13. A. Shashua; “Geometry and Photometry in 3D Visual Recognition”, PhD thesis, MIT, 1992.
  14. V. Blanz, T. Vetter; “A morphable model for the syntheses of 3D faces”, in: Proceedings of International Conference on Computer Graphics, 1999, pp. 187-194.
  15. C. Zhang, F. Cohen; “3-D face structure extraction and recognition from images using 3-D morphing and distance mapping”, IEEE Trans. Image Process. Vol.11 ,2002.
  16. T. Cootes, G. Edwards, C. Taylor; “Active appearance models”, In Proc. European Conf. on Computer Vision, vol. 2, pp 484–498, 1998.
  17. Q. Li, J. Ye, C. Kambhamettu; “Linear Projection Methods in Face Recognition under Unconstrained Illuminations: A Comparative Study,” Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 474- 481, 2004.
  18. S. Choi, C. H. Choi; “An Effective Face Recognition under Illumination and Pose Variations”
  19. T. Vetter, T. Poggio; “ Linear object classes and image synthesis from a single example image, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp.733-742, 1997.
  20. L. Wiskott, J. Fellous, N. Kruger, C. V. Malsburg, “Face recognition by elastic bunch graph matching”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.7, pp.775-779, 1997.
  21. W. T. Freeman, J. B. Tenenbaum, “Learning bilinear models for two-factor problems in vision”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 554 – 560, 1997.
  22. S. Du, R. Ward; “Wavelet based illumination normalization for face recognition”,In Proceedings of international conference on image processing , vol. 2, pp. 954-957,2005.
  23. Y. Gao, M.K.H. Leung; “Face Recognition using line edge map”, IEEE Trans.Pattern Anal. Machine Intell., vol.24, no.6, pp. 764-779,2002.
Index Terms

Computer Science
Information Sciences

Keywords

Pattern Recognition Face Geometry Poses face length