Abstract
Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. It is a piece of cake to collect more than hundreds of thousands of examples from web and digital camera nowadays. How to train a face detector based on the collected immense face database? This paper presents a manifold-based method to select a training set. That is to say we learn the manifold from the collected enormous face database and then subsample and interweave the training set by the estimated geodesic distance in the low-dimensional manifold embedding. By the resulting training set, we train an AdaBoost-based face detector. The trained detector is tested on the MIT+CMU frontal face test set. The experimental results show that the proposed method based on the manifold is efficient to train a classifier confronted with the huge database.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Inform. Proc. Systems, vol. 14, pp. 585–591. MIT Press, Cambridge (2002)
Bernstein, M., de Silva, V., Langford, J., Tenenbaum, J.: Graph approximations to geodesics on embedded manifolds. Technical report, Stanford University (2000)
Brand, M.: Charting a manifold. In Advances in Neural Information. In: Proc. Systems, vol. 15, pp. 961–968. MIT Press, Cambridge (2003)
Chen, J., Chen, X., Gao, W.: Expand Training Set for Face Detection by GA Resampling. In: The 6th IEEE Intern. Conf. FG 2004, pp. 73–79 (2004)
Donoho, D.L., Grimes, C.: When does ISOMAP recover natural parameterization of families of articulated images? Technical Report 2002-27, Stanford University (2002)
Heisele, B., Poggio, T., Pontil, M.: Face Detection in Still Gray Images. CBCL Paper #187. MIT, Cambridge, MA (2000)
Hsu, R.L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Machine Intell, 696–706 (2002)
Hundley, D.R., Kirby, M.J.: Estimation of topological dimension. In: Proc. SIAM International Conference on Data Mining (2003), http://www.siam.org/meetings/sdm03/proceedings/sdm03_18.pdf
Jenkins, O.C., Mataric, M.J.: Automated derivation of behavior vocabularies for autonomous humanoid motion. In: Proc. of the Second Int’l Joint Conference on Autonomous Agents and Multiagent Systems, Melbourne, Australia (July 2003)
Law, M.H., Zhang, N., Jain, A.K.: Nonlinear Manifold Learning for Data Stream. In: Proc. of SIAM Data Mining, Florida, pp. 33–44 (2004)
Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.-Y.: Statistical learning of multi-view face detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002)
Liu, C., Shum, H.Y.: Kullback-Leibler Boosting. In: Proceedings of the 2003 IEEE Conf. on Computer Vision and Pattern Recognition, CVPR 2003 (2003)
Liu, C.J.: A Bayesian Discriminating Features Method for Face Detection. IEEE Trans. Pattern Anal. and Machine Intel., 725–740 (2003)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: An application to face detection. In: Proc. IEEE Conf. on CVPR, pp. 130–136 (1997)
Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Proc. 6th Int. Conf. Computer Vision, pp. 555–562 (1998)
Pettis, K., Bailey, T., Jain, A.K., Dubes, R.: An intrinsic dimensionality estimator from near-neighbor information. IEEE Trans. of Patt. Anal. and Machine Intel., 25–36 (1979)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Roweis, S.T., Saul, L.K., Hinton, G.E.: Global coordination of local linear models. In: Advances in Neural Information Processing Systems, vol. 14, pp. 889–896. MIT Press, Cambridge (2002)
Rowley, H.A., Baluja, S., Kanade, T.: Neural Network-Based Face Detection. IEEE Tr. Pattern Analysis and Machine Intel., 23–38 (1998)
Rowley, H.A., Baluja, S., Kanade, T.: Rotation Invariant Neural Network-Based Face Detection. In: Conf. Computer Vision and Pattern Rec., pp. 38–44 (1998)
Schneiderman, H., Kanade, T.: A Statistical Method for 3D Object Detection Applied to Faces. In: Comp. Vision and Pattern Recog., pp. 746–751 (2000)
Sung, K.K., Poggio, T.: Example-Based Learning for View-Based Human Face Detection. IEEE Trans. on PAM, 39–51 (1998)
Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Conf. Comp. Vision and Pattern Recog., pp. 511-518 (2001)
Teh, Y.W., Roweis, S.T.: Automatic alignment of local representations. In: Advances in Neural Information Processing Systems, vol. 15, pp. 841–848. MIT Press, Cambridge (2003)
Tenenbaum, B.J., Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Verbeek, J.J., Vlassis, N., Krose, B.: Coordinating principal component analyzers. In: Proc. of International Conf. on Artificial Neural Networks, Spain, pp. 914–919 (2002)
Verbeek, J.J., Vlassis, N., Krose, B.: Fast nonlinear dimensionality reduction with topology preserving networks. In: Proc. 10th European Symposium on Artificial Neural Networks, pp. 193–198 (2002)
Xiao, R., Li, M.J., Zhang, H.J.: Robust Multipose Face Detection in Images. IEEE Trans on Circuits and Systems for Video Technology 14(1), 31–41 (2004)
Yang, M.-H.: Face recognition using extended ISOMAP. In: ICIP, pp. 117–120 (2002)
Yang, M.H., Roth, D., Ahuja, N.: A SNoW-Based Face Detector. In: Advances in Neural Information Processing Systems, vol. 12, pp. 855–861. MIT Press, Cambridge (2000)
Yang, M.H., Kriegman, D., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Tr. Pattern Analysis and Machine Intelligence 24, 34–58 (2002)
Zha, H., Zhang, Z.: Isometric embedding and continuum ISOMAP. In: ICML (2003), http://www.hpl.hp.com/conferences/icml2003/papers/8.pdf
http://www.ai.mit.edu/projects/cbcl/software-dataset/index.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, R., Chen, J., Yan, S., Gao, W. (2005). Face Detection Based on the Manifold. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_22
Download citation
DOI: https://doi.org/10.1007/11527923_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
eBook Packages: Computer ScienceComputer Science (R0)