Definition
A major problem in face recognition is to design algorithms that are invariant to those image changes typically observed when capturing faces in real environments. A large group of important image variations can be addressed using a component-based approach, where each face is first analyzed by parts and then the results are combined to provide a global solution. The image variations that are generally tackled with this approach are those due to occlusion, expression, and pose [1]. It has been argued that these changes have a lesser effect on local regions than to the whole of face image. Differences exist on how to formulate the component-based approach. Some of the algorithms use local information and combine these using a global decision maker. Some extract the important local parts to represent the face distributions, while others learn the distribution of the components generated by the...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Martinez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)
P. Belhumeur, D.K.: What is the set of images of an object under all possible illumination conditions? Int. J. Comput. Vis. 28(3), 245–260 (1998)
Lades, M., Vorbruggen, J.C., Buhmann, J., Lange, J., Vandermalsburg, C., Wurtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42(3), 300–311 (1993)
Wiskott, L., Fellous, J.M., Kruger, N., vonderMalsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)
Martinez, A.M.: Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head, SC, USA (2000)
Martinez, A., Benavente, R.: The AR-face database. Tech. rep., CVC Tech. Report # 24 (1998)
Zhang, Y.B., Martinez, A.M.: A weighted probabilistic approach to face recognition from multiple images and video sequences. Image Vis. Comput. 24(6), 626–638 (2006)
Park, B.G., Lee, K.M., Lee, S.U.: Face recognition using Face-ARG matching. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1982–1988 (2005)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Guillamet, D., Bressan, M., Vitrià, J.: Weighted non-negative matrix factorization for local representations. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA, pp. 942–947 (2001)
Zana, Y., Cesar, R.M., Feris, R., Turk, M.: Local approach for face verification in polar frequency domain. Image Vis. Comput. 24(8), 904–913 (2006)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1990)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The feret database and evaluation procedure for face recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)
Heisele, B., Serre, T., Poggio, T.: A component-based framework for face detection and identification. Int. J. Comput. Vis. 74(2), 167–181 (2007)
Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1695–1700 (2006)
Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1475–1490 (2004)
Carlson, A.J., Cumby, C., Rosen, J., Roth, D.: The snow learning architecture. Tech. rep., Technical Report UIUCDCS-R-99-2101, Computer Science Department, University of Illinois at Urbana-Champaign (1999)
Guo, G., Dyer, C.: Patch-based image correlation with rapid filtering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Minneapolis, Minnesola, USA (2007)
Martinez, A.M.: Face image retrieval using hmms. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Workshop) Ft. Collins, CO, USA (1999)
Ke, Y., Sukthankar, R.: Pca-sift: A more distinctive representation for local image descriptors. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC, USA (2004)
Zou, J., Ji, Q., Nagy, G.: A comparative study of local matching approach for face recognition. IEEE Trans. Image Process. 16(10), 2617–2628 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
Hamsici, O.C., Martinez, A.M. (2009). Face Recognition, Component-Based. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_93
Download citation
DOI: https://doi.org/10.1007/978-0-387-73003-5_93
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-73002-8
Online ISBN: 978-0-387-73003-5
eBook Packages: Computer ScienceReference Module Computer Science and Engineering