nips nips2001 nips2001-46 nips2001-46-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso Poggio
Abstract: We describe an algorithm for automatically learning discriminative components of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classifiers are then combined in a second stage to yield a hierarchical SVM classifier. Experimental results in face classification show considerable robustness against rotations in depth and suggest performance at significantly better level than other face detection systems. Novel aspects of our approach are: a) an algorithm to learn component-based classification experts and their combination, b) the use of 3-D morphable models for training, and c) a maximum operation on the output of each component classifier which may be relevant for biological models of visual recognition.
[1] B. Heisele, P. Ho, and T. Poggio. Face recognition with support vector machines: global versus component-based approach. In Proc. 8th International Conference on Computer Vision, Vancouver, 2001.
[2] B. Heisele, T. Poggio, and M. Pontil. Face detection in still gray images. A.I. memo 1687, Center for Biological and Computational Learning, MIT, Cambridge, MA, 2000.
[3] B. Heisele, T. Serre, S. Mukherjee, and T. Poggio. Feature reduction and hierarchy of classifiers for fast object detection in video images. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, 2001.
[4] T. K. Leung, M. C. Burl, and P. Perona. Finding faces in cluttered scenes using random labeled graph matching. In Proc. International Conference on Computer Vision, pages 637–644, Cambridge, MA, 1995.
[5] A. Mohan, C. Papageorgiou, and T. Poggio. Example-based object detection in images by components. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 23, pages 349–361, April 2001.
[6] C. Papageorgiou and T. Poggio. A trainable system for object detection. In International Journal of Computer Vision, volume 38, 1, pages 15–33, 2000.
[7] T. Poggio and S. Edelman. A network that learns to recognize 3-D objects. Nature, 343:163–266, 1990.
[8] M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11):1019–1025, 1999.
[9] T. D. Rikert, M. J. Jones, and P. Viola. A cluster-based statistical model for object detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 1046–1053, Fort Collins, 1999.
[10] H. A. Rowley, S. Baluja, and T. Kanade. Rotation invariant neural network-based face detection. Computer Science Technical Report CMU-CS-97-201, CMU, Pittsburgh, 1997.
[11] H. Schneiderman and T. Kanade. A statistical method for 3d object detection applied to faces and cars. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 746–751, 2000.
[12] T. Sim, S. Baker, and M. Bsat. The CMU pose, illumination, and expression (PIE) database of human faces. Computer Science Technical Report 01-02, CMU, 2001.
[13] K.-K. Sung. Learning and Example Selection for Object and Pattern Recognition. PhD thesis, MIT, Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Cambridge, MA, 1996.
[14] R. Vaillant, C. Monrocq, and Y. Le Cun. An original approach for the localisation of objects in images. In International Conference on Artificial Neural Networks, pages 26–30, 1993.
[15] V. Vapnik. Statistical learning theory. John Wiley and Sons, New York, 1998.
[16] T. Vetter. Synthesis of novel views from a single face. International Journal of Computer Vision, 28(2):103–116, 1998.