Abstract
Principal components analysis has become a popular preprocessing method to avoid the small sample size problem for most of the supervised graph embedding methods. Nevertheless, there is potential loss of relevant information when projecting the data onto the space defined by the principal Eigenfaces when the number of individuals in the gallery is large. This paper introduces a new collaborative feature extraction method based on projection pursuit, as a robust preprocessing for supervised embedding methods. A previously proposed projection index was adopted as a measure of interestingness, based on a weighted sum of six state of the art indices. We compare our collaborative feature extraction technique against principal component analysis as preprocessing stage for Laplacianfaces. For completeness, results for Eigenfaces and Fisherfaces are included. Experimental results to demonstrate the robustness of our approach against changes in facial expression and lighting are presented.
Similar content being viewed by others
References
Batur AU, Hayes MH (2001) Linear subspace for illumination robust face recognition. In: Proceedings of the IEEE international conference on computer vision and patter recognition, Dec 2001
Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of the conference advances in neural information processing system, vol 15. MIT Press, Cambridge
Cai D, He X, Han J, Zhang HJ (2006) Orthogonal Laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614
Chiang S, Chang C, Ginsberg I (2001) Unsupervised target detection in hyperspectral images using projection pursuit. IEEE Trans Geosci Remote Sens 39(7):1380–1391
Deng W, Hu J, Guo J, Cai W, Feng D (2010) Robust, accurate and efficient face recognition from a single training image: a uniform pursuit approach. Pattern Recogn 43:1748–1762
Friedman H, Tukey J (1974) A projection pursuit algorithm for exploratory data analysis. IEEE Trans Comput C-23:881–889
Gross R, Shi J, Cohn J (2001) Where to go with face recognition. In: Proceedings of the third workshop empirical evaluation methods in computer vision, Dec 2001
Guo Q, Wu W, Massart D, Boucon C, de Jong S (2000) Sequential projection pursuit using genetic algorithms for data mining of analytical data. Ann Chem 72:2846–2855
He X, Niyogi P (2003) Locality preserving projections. In: Proceeding of the conference on advances in neural information processing systems, vol 16. MIT Press, Cambridge
He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Recognit Mach Intell 27(3):328–340
Huber P (1985) Projection pursuit. Ann Stat 13:435–475
Hyvarinen A (1999) Survey on independent component analysis. Neural Comput Surv 2(1):94–128
Ifarraguerri A, Chang C (2000) Unsupervised hyperspectral image analysis with projection pursuit. IEEE Trans Geosci Remote Sens 38(6):2529–2538
Jimenez L, Landgrebe D (1999) Hyperspectral data analysis and supervised feature reduction via projection pursuit. IEEE Trans Geosci Remote Sens 37(6):2653–2667
Jimenez L, Arzuga E, Velez M (2007) Unsupervised linear feature-extraction methods and their effects in the classification of high dimensional data. IEEE Trans Geosci Remote Sens 45(2):469–483
Jimenez AB, Lazaro JL, Dorronsoro JR (2009) Finding optimal model parameters by deterministic and annealed focused grid search. Neurocomputing 72(13-15):2824–2832
Jones M, Sibson R (1987) What is projection pursuit?. J R Stat Soc 150(1):1–37
Kokiopoulou E, Saad Y (2007) Orthogonal neighbourhood preserving projections. IEEE Trans Pattern Anal Mach Intell 29(12):2143–2156
Kokiopoulou E, Saad Y (2009) Enhanced graph-based dimensionality reduction with repulsion Laplaceans. Pattern Recogn 42:2392–2402
Levin A, Shashua A (2003) Principal component analysis over continuous subspaces and intersection of half-spaces. In: Proceedings of the European conference on computer vision and pattern recognition, vol 1, pp 313–320
Li SZ, Hou XW, Zhang HJ, Cheng QS (2001) Learning spatially localized, parts-based representation. In: Proceedings of IEEE international conference on computer vision and pattern recognition, Dec 2001
Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):157–165
Liu C, Wechsler H (2000) Evolutionary pursuit and its application to face recognition. IEEE Trans Pattern Anal Mach Intell 22(6):560–582
Martinez AM, Kak AC (2002) Face recognition using kernel based Fisher discriminant analysis. In: Proceedings of the fifth international conference on automatic face and gesture recognition, May 2002
Moses Y, Adini Y, Ulman S (1994) Face recognition: the problem of compensating for changes in illumination direction. In: European conference on computer vision, pp 286–296
Nason G (2001) Robust projection indices. J R Stat Soc B 63(3):551–567
Rodriguez E, Nikolaidis K, Mu T, Ralph JF, Goulermas JY (2010) Collaborative projection pursuit for face recognition. In Proceedings of the fifth IEEE international conference on bio-inspired computing: theories and applications, pp 1346–1350, Sept 2010
Rodriguez-Martinez E, Goulermas JY, Mu T, Ralph JF (2010) Automatic induction of projection pursuit indices. IEEE Trans Neural Netw 21(8):1281–1295
Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326
Seung HS, Lee DD (2000) The manifold ways of perception. Science 290(5500):2268–2269
Shakunaga T, Shigenari K (2001) Decomposed Eigenfaces for face recognition under various lighting conditions. In: Proceedings of the IEEE international conference on computer vision and pattern recognition, Dec 2001
Sirovitch L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 2:519–524
Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J Mach Learn Res 8:1027–1061
Tenenbaum J, de Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
Webb-Robertson BJM, Jaram KH, Harvey SD, Pose C, Wright BW (2005) An improved optimization algorithm and a Bayes termination criterion for sequential projection pursuit. Chemom Intell Lab Syst 77(1–2):149–160
Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):1027–1061
Yang J, Yu Y, Kunz W (2000) An efficient LDA algorithm for face recognition. In: Proceedings of the sixth international conference on control, automation, robotics and vision
Yang J, Yu Y, Niu B (2007) Globally maximizing locally minimizing: unsupervised discriminant projection with application to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664
Zhang S (2009) Enhanced supervised locally linear embedding. Pattern Recogn Lett 30(13):1208–1218
Zhang K, Chan L (2006) Dimension reduction as a deflation method in ICA. IEEE Signal Process Lett 13(1):45–48
Zhang T, Huang K, Li X, Yang J, Tao D (2010) Discriminative orthogonal neighborhood-preserving projections for classification.IEEE Trans Syst Man Cybern B Cybern 40(1):253–263
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rodriguez, E., Nikolaidis, K., Mu, T. et al. Towards collaborative feature extraction for face recognition. Nat Comput 11, 395–404 (2012). https://doi.org/10.1007/s11047-011-9285-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11047-011-9285-6