A Case Study on Appearance Based Feature Extraction Techniques and Their Susceptibility to Image Degradations for the Task of Face Recognition
Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in "real-life" operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071608Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2137
 M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neurosicence, vol. 3, no. 1, pp. 71-86, 1991.
 P.N. Belhumeur, J.P. Hespanha and D.J. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class specific linear projection," in Proc. of the 4th European Conference on Computer Vision, ECCV-96, Cambridge, UK, 1996, pp. 45-58.
 M.S. Bartlett, J.R. Movellan ans T.J. Sejnowski, "Face recognition by independent component analysis," IEEE Trans. on Neural Networks, vol. 13, pp. 1450-1464, November 2002.
 C. Liu and H. Wechsler, "Comparative assessment of independent component analysis (ICA) for face recognition," in Proc. of the 2nd International C onference on Audio- and Video-based Biometric Person Authentication, AVBPA-99, Washington D.C., USA, 1999, pp. 211-216.
 K. Baek, B. Draper, J.R. Beveridge and K. She, "PCA vs. ICA: a comparison on the FERET data set," in Proc. of the 4th International Conference on Computer Vision, ICCV-02, Durham, 2002, pp. 824-827.
 K. Delac, M. Grgic and S. Grgic, "Independent comparative study of PCA, ICA, and LDA on the FERET data set," International Journal of Imaging Systems and Technology, vol. 15, no. 5, pp. 252-260, 2006.
 J.R. Beveridge, K. She, B. Draper and G.H. Givens, "A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR-01, Kauai, 2001, pp. 535-542.
 Y. Li, J. Kittler and J. Matas, "Effective implementation of linear discriminant analysis for face recognition and verification," in Proc. of the 8th International Conference on Computer Analysis of Images and Patterns, CAIP-99, Ljubljana, 1999, pp. 234-242.
 V. ╦ç Struc and N. Pave╦çsi'c, "Gabor-based kernel partial-least-squares discrimination features for face recognition," International Journal Informatica, to be published.
 V. ╦ç Struc and N. Pave╦çsi'c, "The corrected normalized correlation coefficient : a novel way of matching score calculation for LDA-based face verification," in Proc. of the 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD-08, Jinan, 2008, pp. 110-115.
 B. Batagelj and F. Solina, "Face recognition in different subspaces - a comparative study," in Proc. of the 6th International Workshop on Pattern Recognition in Information Systems, PRIS-06, Paphos, 2006, pp.71-80.
 The FastICA package for Matlab - accessed March 2009. Available: http://www.cis.hut.fi/projects/ica/fastica/
 A. Hyvarinen ans E. Oja, "Independent component analysis: algorithms and aplications," Neural Networks vol. 13, no. 4-5, pp. 411430, 2000.
 A.K. Jain, A. Ross and S. Prabhakar, "An introduction to biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, pp. 4-20, January 2004.
 O. Jesorsky, K.J. Kirchberg and R.W. Frischholz, "Robust face detection using the Hausdorff distance," in: Proc. of the 3rd Internatiojnal Conference on Audio- and Video-based Biometric Person Authentication, AVBPA-01, Halmstad, 2001, pp.90-95.
 A. Pozne, "Extracting visual features for automated speech recognition," Ph.D. dissertation, Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, 2005.
 K. Messer, J. Matas, J. Kittler, J. Luettin and G. Maitre, "XM2VTSDB: the extended M2VTS database," in Proc. of AVBPA-99, Washington D.C., 1999, pp. 72-77.
 N. Pave╦çsi'c, I. Fratri'c and S. Ribari'c, "Degradation of the XM2VTS database face images," in Proc. 2nd COST 275 Workshop, Vigo, 2004, pp. 15-19.