Abstract
This chapter reports recent advances in the statistical learning literature that may be of interest for biometrics. In particular we discuss two different algorithmic settings, binary classification and multi-task learning, and analyze the two closely related problems of feature selection and feature learning. In the binary case the theoretical and algorithmic advances to feature selection are applied to solve face detection and face authentication problems. In the multi-task case we show how the data structure described by a group of features common to the various tasks can be effectively learned, and then we discuss how this approach could be used to address face recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
G. Aggarwal, A. Chowdhury, and R. Chellappa., A system identification approach for video-based face recognition., In Proc. International Conference on Pattern Recognition, pages 175–178, 2004.
T. Ahonen, A. Hadid, and M. Pietikainen., Face description with local binary patterns: application to face recognition., IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(12):2037–2041, 2006.
R. K. Ando and T. Zhang., A framework for learning predictive structures from multiple tasks and unlabeled data., Journal of Machine Learning Research, 6:1817–1853, 2005.
A. Argyriou, T. Evgeniou, and M. Pontil., Convex multi-task feature learning., Machine Learning, 73(3):243–272, 2008.
A. Argyriou, T. Evgeniou, and M. Pontil., Multi-task feature learning., In B. Schülkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19. MIT Press, 2006.
A. Argyriou, T. Evgeniou, and M. Pontil., Multi-task feature learning., In B. Schülkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19. MIT Press, 2007., In press.
J. Baxter., A model for inductive bias learning., Journal of Artificial Intelligence Research, 12:149–198, 2000.
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman., Eigenfaces versus fisherfaces., IEEE Trans. on Pattern Analysis and Machine Intelligence, 19:711–720, 1997.
M. Bicego, A. Lagorio, E. Grosso, and M. Tistarelli., On the use of sift features for face authentication., In Proc. of IEEE Int Workshop on Biometrics, in association with CVPR06, page 35ff, 2006.
R. Brunelli and T. Poggio., Face recognition: features versus templates., IEEE Trans. on Pattern Analysis and Machine Intelligence, 15:1042–1052, 1993.
S. S. Chen, D. Donoho, and M. Saunders., Atomic decomposition by basis pursuit., SIAM Journal of Scientific Computing, 20(1), 1998.
I. Daubechies, M. Defrise, and C. De Mol., An iterative thresholding algorithm for linear inverse problems with a sparsity constraint., Communications on Pure Applied Mathematics, 57, 2004.
A. Destrero, C. De Mol, F. Odone, and A. Verri., A regularized approach to feature selection for face detection., Technical Report DISI-TR-07-01, Dipartimento di informatica e scienze dell’informazione, Universita’ di Genova, 2007.
A. Destrero, C. De Mol, F. Odone, and A. Verri., A regularized approach to feature selection for face detection., In Y. Yagi et al., editor, Proc. of the Asian Conference on Computer Vision, ACCV, LNCS 4844, pages 881–890, 2007.
A. Destrero, S. Mosci, C. De Mol, A. Verri, and F. Odone., Feature selection for high dimensional data., Computational Management Science, 6(1):25–40 (2009).
A. Destrero, F. Odone, and A. Verri., A system for face detection and tracking in unconstrained environments., In IEEE International Conference on Advanced Video and Signal-based Surveillance, In Proceedings IEEE AVSS 2007, pages 499–504, 2007.
D. Donoho. High-dimensional data analysis: The curses and blessings of dimensionality. Aide-Memoire of a Lecture at AMS conference on Math Challenges of 21st Century. Available at http://www-stat.stanford.edu/donoho/Lectures/AMS2000/AMS2000.html
G. J. Edwards, C. J. Taylor, and T. F. Cootes., Improving identification performance by integrating evidence from sequences., Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 1, pp. 1486, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’99) – Volume 1, 1999.
K. Etemad and R. Chellappa., Discriminant analysis for recognition of human face images., Journal of the Optical Society of America A, 14:1724–1733, 1997.
M. Fazel, H. Hindi, and S. P. Boyd., A rank minimization heuristic with application to minimum order system approximation., In Proceedings, American Control Conference, 4734–4739, 2001.
Y. Freund and R. E. Schapire., A decision-theoretic generalization of on-line learning and an application to boosting., In European Conference on Computational Learning Theory, pages 23–37, 1995.
J. Friedman, T. Hastie, and R. Tibshirani., Additive logistic regression: a statistical view of boosting, 1998.
D. O. Gorodnichy., On importance of nose for face tracking., In IEEE International conference on automatic face and gesture recognition, pages 181–186, 2002.
I. Guyon and E. Elisseeff., An introduction to variable and feature selection., Journal of Machine Learning Research, 3:1157–1182, 2003.
A. Hadid, M. Pietikäinen, and S. Z. Li., Learning personal specific facial dynamics for face recognition from videos., In Analysis and Modeling of Faces and Gestures, pages 1–15, Springer LNCS 4778, 2007.
X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang., Face recognition using laplacianfaces., IEEE Trans. Pattern Analysis and Machine Intelligence, 27:328-340, 2005.
B. Heisele, P. Ho, J. Wu, and T. Poggio., Face recognition: component-based versus global approaches., Computer Vision and Image Understanding, 91:6–21, 2003.
B. Heisele, T. Serre, M. Pontil, and T. Poggio., Component-based face detection., In CVPR, 2001.
A. K. Jain, A. Ross, and S. Prabhakar., An introduction to biometric recognition., IEEE Trans. on Circuits and Systems for Video Technology, 14(1), 2004.
T. Jebara., Multi-task feature and kernel selection for SVMs., In Proceedings of the 21st International Conference on Machine Learning, 2004.
K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, A. Kostyn, S. Marcel, S. Bengio, F. Cardinaux, C. Sanderson, N. Poh, Y. Rodriguez, K. Kryszczuk, J. Czyz, L. Vandendorpe, J. Ng, H. Cheung, and B. Tang., Face authentication competition on the banca database., In Biometric Authentication, LNCS 3072, 2004.
B. Li and R. Chellappa., Face verification through tracking facial features., Journal of the Optical Society of America, JOSA-A, 18(12):2969–2981, 2001.
S. Z. Li and Z. Q. Zhang., FloatBoost learning and statistical face detection., IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 2004.
R. Lanzarotti, S. Arca, P. Campadelli., A face recognition system based on automatically determined facial fiducial points., Pattern Recognition, 39(3):432–443, 2006.
X. Liu, T. Chen, and B. V. K. Vijaya Kumar., On modeling variations for face authentication., Pattern Recognition, 36(2):313–328, 2003.
B. Moghaddam and A. Pentland., Probabilistic visual learning for object representation., IEEE Trans. on Pattern Analysis and Machine Intelligence, 19:696–710, 1997.
A. Mohan, C. Papageorgiou, and T. Poggio., Example-based object detection in images by components., IEEE Trans. on PAMI, 23(4):349–361, 2001.
C. de Mol, S. Mosci, M. S. Traskine, and A. Verri., Sparsity enforcing and correlation preserving algorithm for microarray data analysis., Technical Report DISI-TR-07-04, Dipartimento di informatica e scienze dell’informazione, Universita’ di Genova, 2007.
T. Ojala, M. Pietikainen, and T. Maenpaa., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns., IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(7), 2002.
E. Osuna, R. Freund, and F. Girosi., Training support vector machines: an application to face detection., Computer Vision and Pattern Recognition, IEEE Computer Society Conference on (CVPR’97), pp. 130, 1997
A. Pentland, B. Moghaddam, and T. Starner., View-based and modular eigenspaces for face recognition., In IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), page 84-91, 1994.
A. Pentland, B. Moghaddam, and T. Starner., Estimation of eye, eyebrow and nose features in videophone sequences., In International Workshop on Very Low Bitrate Video Coding (VLBV 98), page 101Â-104, 1998.
M. Pontil and A. Verri., Support vector machines for 3-d object recognition., IEEE Trans-actions on Pattern Analysis and Machine Intelligence, 20:637–646, 1998.
D. Roth, M. Yang, and N. Ahuja., A snowbased face detector., Neural Information Processing, 12, 2000.
H. Rowley, S. Baluja, and T. Kanade., Neural network-based face detection., IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:22–38, 1998.
H. Schneiderman and T. Kanade., A statistical method for 3D object detection applied to faces and cars., In International Conference on Computer Vision, 2000.
J. Sergent., Microgenesis of face perception., In H. D. Ellis, M. A. Jeeves, F. Newcombe, and A. M. Young, editors, Aspects of face processing (pp. 17–33). Dordrecht: Martinus Nijhoff, 1986
S. Soatto, G. Doretto, and Y. Wu., Dynamic textures., In Proc of the International Conference on Computer Vision, pages 439–446, 2001.
N. Srebro, J. D. M. Rennie, and T. S. Jaakkola., Maximum-margin matrix factorization., In Advances in Neural Information Processing Systems, 17, pages 1329–1336. MIT Press, 2005.
K. Sung and T. Poggio., Example-based learning for view-based face detection., IEEE Transactions on PAMI, 20, 1998.
R. Tibshirani., Regression shrinkage and selection via the lasso., Journal of the Royal Statistical Society B, 58(1):267–288, 1996.
A. Torralba, K. P. Murphy, and W. T. Freeman., Sharing features: efficient boosting procedures for multiclass object detection., In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2: 762–769, 2004.
M. A. Turk and A. P. Pentland., Eigenfaces for recognition., Journal of Cognitive Neuroscience, 3(1):71–86, 1991.
S. Ullman, M. Vidal-Naquet, and E. Sali., Visual features of intermediate complexity and their use in classification., Nature Neuroscience, 5(7), 2002.
V. N. Vapnik., Statistical Learning Theory., Wiley, 1998.
P. Viola and M. J. Jones., Robust real-time face detection., International Journal on Computer Vision, 57(2):137–154, 2004.
J. Weston, A. Elisseeff, B. Scholkopf, and M. Tipping., The use of zero-norm with linear models and kernel methods., Journal of Machine Learning Research, 3, 2003.
L. Wiskott, J. Fellous, N. Kuiger, and C. von der Malsburg., Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:775–779, 1997.
M.-H. Yang, D. J. Kriegman, and N. Ahuja., Detecting faces in images: a survey., IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(1):34–58, 2002.
W. Zhao, R. Chellappa, A. Rosenfeld, and P.J. Phillips., Face recognition: a literature survey., ACM Computing Surveys, 35(4):399–458, 2003.
J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani., 1-norm support vector machines., In Advances in Neural Information Processing SYstems, 16. MIT Press, 2004.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag London Limited
About this chapter
Cite this chapter
Odone, F., Pontil, M., Verri, A. (2009). Machine Learning Techniques for Biometrics. In: Tistarelli, M., Li, S.Z., Chellappa, R. (eds) Handbook of Remote Biometrics. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-385-3_10
Download citation
DOI: https://doi.org/10.1007/978-1-84882-385-3_10
Publisher Name: Springer, London
Print ISBN: 978-1-84882-384-6
Online ISBN: 978-1-84882-385-3
eBook Packages: Computer ScienceComputer Science (R0)