Call for Paper - October 2019 Edition
IJCA solicits original research papers for the October 2019 Edition. Last date of manuscript submission is September 20, 2019. Read More

Illumination Invariant Facial Pose Classification

Print
PDF
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 37 - Number 1
Year of Publication: 2012
Authors:
Ajay Jaiswal
Nitin Kumar
R. K. Agrawal
10.5120/4571-6565

Ajay Jaiswal, Nitin Kumar and R K Agrawal. Article: Illumination Invariant Facial Pose Classification. International Journal of Computer Applications 37(1):14-19, January 2012. Full text available. BibTeX

@article{key:article,
	author = {Ajay Jaiswal and Nitin Kumar and R. K. Agrawal},
	title = {Article: Illumination Invariant Facial Pose Classification},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {37},
	number = {1},
	pages = {14-19},
	month = {January},
	note = {Full text available}
}

Abstract

In this paper, we compared the performance of various combinations of edge operators and linear subspace methods to determine the best combination for pose classification. To evaluate the performance, we have carried out experiments on CMU-PIE database which contains images with wide variation in illumination and pose. We found that the performance of pose classification depends on the choice of edge operator and linear subspace method. The best classification accuracy is obtained with Prewitt edge operator and Eigenfeature regularization method. In order to handle illumination variation, we used adaptive histogram equalization as a preprocessing step resulting into significant improvement in performance except for Roberts operator.

References

  • E. M. Chutorian and M. M. Trivedi. Head Pose Estimation in Computer Vision: A Survey, IEEE Trans. on PAMI, 31(4): 607 – 626, 2009.
  • S.Z. Li and A.K. Jain. Hand book of face recognition. Springer-Verlag, 2005.
  • H. S. Lee and D. Kim. Generating frontal view face image for pose invariant face recognition, PR letters, 27(7):747-754, 2006.
  • S. Choi, C. Choi and N. Kwak. Face recognition based on 2D images under illumination and pose variations, PR Letters, 32(4):561–571, 2011.
  • M.S. Sarfraz and O. Hellwich. Head Pose Estimation in Face Recognition Across Pose Scenarios, in Proc. VISAPP (1):235-242, 2008.
  • S. Zhao and Y. Gao. Automated Face Pose Estimation Using Elastic Energy Models, The 18th ICPR, 4:618-621, 2008.
  • S. Gong, S. McKenna, and J. J. Collins. An investigation into face pose distributions, In FG., 265, 1996.
  • M. Gründig and O. Hellwich. 3D Head Pose Estimation with Symmetry Based Illumination Model in Low Resolution Video, DAGM, LNCS, 45-53, 2004.
  • S. Choi, C. Choi and N. Kwak. Feature extraction for regression problems and an example application for pose estimation of a face, In Proc. fifth International Conference on Image Analysis and Recognition – ICIAR, 435–444, 2008.
  • R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, second edition, 1992.
  • J. Ye and Q. Li . A two-stage linear discriminant analysis via qr-decomposition, IEEE Trans. on PAMI, 27:929-941,2005.
  • L. Chen, H. Liao, M. Ko, J. Lin, and G. Yu. A New LDA Based Face Recognition System Which can Solve the Small Sample Size Problem, Journal of Pattern Recognition, 33(10): 1713-1726, 2000.
  • X. Wang and X. Tang. Dual-Space Linear Discriminant Analysis for Face Recognition, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04), 2: 564-569, 2004.
  • X. Jiang, B. Mandal, and A. Kot. Eigenfeature Regularization and Extraction in Face Recognition, IEEE Trans. on PAMI, 30(3): 383-394, 2008.
  • S.K. Kim, Y. J. Park, K.A. Toh, and S. Lee. SVM-based feature extraction for face recognition. Pattern Recognition, 43(8): 2871-2881, 2010.
  • M. A. Oskoei and H. Hu. A Survey on Edge Detection Methods, Technical report, 2010.
  • B. G. Schunck. Edge detection with Gaussian filters at multiple scales, in Proc. IEEE Comp. Soc. Work. Comp. Vis., 208–210, 1987.
  • P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion, IEEE Trans. on PAMI, 12:629–639, 1990.
  • D. Heric and D. Zazula. Combined edge detection using wavelet transform and signal registration, Elsevier Journal of Image and Vision Computing, 25:652–662, 2007.
  • S. Konishi, A. L. Yuille, J. M. Coughlan, and S. C. Zhu. Statistical Edge Detection: Learning and Evaluating Edge Cues, IEEE Transactions on PAMI, 25(1):57-74, 2003.
  • J. Wu, Z. Yin and Y. Xiong. The Fast Multilevel Fuzzy Edge Detection of Blurry Images, IEEE Signal Processing Letters, 14(5):344-347, 2007.
  • Y. Yu and C. Chang. A new edge detection approach based on image context analysis, Elsevier Journal of Image and Vision Computing 24:1090–1102, 2006.
  • G. Giraudon. Edge Detection from Local Negative Maximum of Second Derivative. In Proceedings of IEEE, International Conference on CVPR, 643-645, 1985.
  • J. Canny. A computational approach to edge detection, IEEE Trans. on PAMI, 8 (6): 679–698, 1986.
  • R. Duda and P. Hart. Pattern Classification and SceneAnalysis. New York: Wiley, 1973.
  • T. Zhang, B. Fang, Y. Y. Tang, Z. Shang and B. Xu. Generalized Discriminant Analysis: A Matrix Exponential Approach, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 40(1):186-197, 2010.
  • T. Sim, S. Baker, and M. Bsat. The CMU pose, illumination, and expression (PIE) database of human faces. Technical Report CMU-RITR- 01-02, The Robotics Institute, Carnegie Mellon University, 2001.