You are here

Home » Content

FACE RECOGNITION USING PCA, LDA AND ICA APPROACHES ON COLORED IMAGES

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2003

Key Words:

Author Name
Abstract (2. Language): 
In this paper, the performances of appearance-based statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are tested and compared for the recognition of colored face images. Three sets of experiments are conducted for relative performance evaluations. In the first set of experiments, the recognition performances of PCA, LDA and ICA are demonstrated. The effect of illumination variations is evaluated in the second set, whereas input images are partially occluded in the third set of experiments. The results show that PCA is better than LDA and ICA under different illumination variations but LDA is better than ICA. On the other hand, LDA is more sensitive than PCA and ICA on partial occlusions, but PCA is less sensitive to partial occlusions compared to LDA and ICA sensitivity. That is, PCA performance is better than LDA and ICA while performance of ICA performance is better than LDA on partial occlusions.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-face-recognition-using-pca-lda-and-ica-approaches-colored-images.pdf
  • 1
735-743
  • English

REFERENCES

References: 

[1] Zhao W., Chellappa R., Rosenfeld A., and Phillips P. J.. “Face recognition: A literature survey” Technical Report CAR-TR-948, CS-TR-4167, N00014-95-1-0521, October 2000.
[2] Chellappa R., Sirohey S., Wilson C.L., Barnes C.S., “Human and Machine Recognition of Faces: A Survey”, in Proceedings of IEEE, May 95, Vol. 83, pp 705-740.
[3] Fromherz T., Stucki P., Bichsel M., “A Survey of Face Recognition”, MML Technical Report, No 97.01, Dept. of Computer Science, University of Zurich, 1997.
[4] Turk M. and Pentland A., “Face recognition using eigenfaces”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, 1991.
[5] Belhumeur N., Hespanha J. and Kriegman D., “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, Proc. ECCV, 45--58, 1996.
[6] Calvo R., Partridge M., and Jabri M., “A Comparative Study of Principal Components Analysis Techniques”, In Proc. Ninth Australian Conf. on Neural Networks, Brisbane, QLD., pp. 276-281, 1998.
[7] Yambor W.S., “Analysis of PCA-Based and Fisher Discriminant-Based Image Recognition Algorithms”, Technical Report CS-00-103, Computer Science Department, Colorado State University, July 2000.
[8] Martinez A.M. and Kak A.C., “PCA versus LDA”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No.2, pp. 228-233, 2001.
[9] Yang J., Yu Y., Kunz W., “An Efficient LDA Algorithm for Face Recognition”, The Sixth International Conference on Control, Automation, Robotics and Vision (ICARCV2000), 2000.
[10] Spacek L., “Description of Libor Spacek's Collection of Facial Images”, 1996, online: http://cswww.essex.ac.uk/allfaces/index.html
[11] Hyvärinen, A., “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis”, IEEE Transactions on Neural Networks, Vol. 10, No. 3, pp. 626-634, 1999.
[12] Giannakopoulos, X., Karhunen, J. & Oja, E., “A Comparison of Neural ICA Algorithms Using Real-World Data”, International Joint Conference on Neural Networks (IJCNN'99), Washington, D.C., USA, July 10-16, 1999.

Thank you for copying data from http://www.arastirmax.com