Image analysis – Applications – Personnel identification
Reexamination Certificate
2004-06-30
2010-06-08
Bella, Matthew C (Department: 2624)
Image analysis
Applications
Personnel identification
C382S155000, C382S227000
Reexamination Certificate
active
07734071
ABSTRACT:
Systems and methods are presented that determine components to use as examples to train a component-based face recognition system. In one embodiment, an initial component shape and size is determined, a training set is built, a component recognition classifier is trained, and the accuracy of the classifier is estimated. The component is then temporarily grown in each of four directions (up, down, left, and right) and the effect on the classifier's accuracy is determined. The component is then grown in the direction that maximizes the classifier's accuracy. The process can be performed multiple times in order to maximize the classifier's accuracy.
REFERENCES:
patent: 5497430 (1996-03-01), Sadovnik et al.
patent: 5850470 (1998-12-01), Kung et al.
patent: 6108437 (2000-08-01), Lin
patent: 6233365 (2001-05-01), Teruhiko
patent: 6317517 (2001-11-01), Lu
patent: 6421463 (2002-07-01), Poggio et al.
patent: 6671391 (2003-12-01), Zhang et al.
patent: 6975750 (2005-12-01), Yan et al.
patent: 7099510 (2006-08-01), Jones et al.
patent: 7203346 (2007-04-01), Kim et al.
patent: 7218759 (2007-05-01), Ho et al.
patent: 2003/0044067 (2003-03-01), Huang et al.
patent: 2003/0110038 (2003-06-01), Sharma et al.
patent: 2003/0225526 (2003-12-01), Golub et al.
patent: 2004/0064464 (2004-04-01), Forman et al.
patent: 07-065165 (1995-03-01), None
patent: 2003-150963 (2003-05-01), None
patent: WO 2002/039371 (2002-05-01), None
Stewart et al., Region Growing with Pulse-Coupled Neural Networks: An Alternative to Seeded Region Growing, Nov. 2002, IEEE Transactions on Neural Networks, vol. 13, Issue: 16, pp. 1557-1562.
Lin et al., Automatic Facial Feature Extraction by Applying Genetic Algorithms, Jun. 9-12, 1997, International Conference on Neural Networks, 1997, vol. 3, pp. 1363-1367.
Heisele et al., Learning and Vision Machines, Jul. 2002, Proceedings of the IEEE, vol. 90,pp. 1164-1177.
Heisele et al., Component-based Face Detection, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, vol. 1, pp. I-657-I-662.
Heisele et al., Advances in Neural Information Processing Systems: Categorization by Learning and Combining Object Parts, 2002, MIT, vol. 2, Issue 14, unnumbered: 7 total pages.
Heisele et al., Face Detection in Still Gray Images, May 2000, Massachusetts Institute of Technology, pp. 1-25.
Heisele, B. et al., “Component-based Face Detection”, Proceedings Of The IEEE Computer Society Conference On Computer Vision And Pattern Recognition (CVPR) 2001, Kauai, HI, vol. 1, Sep. 10, 2001, pp. 657-662.
PCT International Search Report and Written Opinion of the International Searching Authority, PCT/US2004/021158, Mar. 31, 2005.
Heisele, B. et al., “Learning and Vision Machines,” Proceedings of the IEEE, vol. 90, No. 7, Jul. 2002, pp. 1164-1177.
Huang, J. et al., “Component-Based Face Recognition with 3D Morphable Models,” Proceedings of the 4thInternational Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA), Jun. 9-11, 2003 (Lecture Notes in Computer Science, vol. 2688), Springer-Verlag Berlin, Germany, pp. 27-34.
Kim, T-K et al., “Component-Based LDA Face Descriptor for Image Retrieval,” Proceedings of the British Machine Vision Conference (BMVC), Sep. 2-5, 2002, vol. 2, pp. 507-516.
Viola, Paul, “Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects,” AI Memo No. 1591, Massachusetts Institute of Technology, Artificial Intelligence Laboratory, MA, Nov. 1996, pp. 1-21.
PCT International Search Report, PCT/IB2004/003274, Apr. 6, 2005.
Beymer, D. J.,Face Recognition Under Varying Pose,A.I. Memo 1461, Center for Biological and Computational Learning, M.I.T., Cambridge, MA, 1993.
Blanz, V. et al.,A Morphable Model for the Synthesis of 3D Faces,Computer Graphics Proceedings SIGGRAPH, pp. 187-194, Los Angeles, 1999.
Brunelli, R. et al.,Face Recognition: Features Versus Templates,IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10), pp. 1042-1052, 1993.
Heisele, B. et al.,Face Recognition With Support Vector Machines: Global Versus Component-Based Approach,Proc. 8thInternational Conference on Computer Vision, vol. 2, pp. 688-694, Vancouver, 2001.
Heisele, B. et al.,Categorization by Learning and Combining Object Parts,Neural Information Processing Systems (NIPS), pp. 1239-1245, Vancouver, 2001.
Wallraven, C et al.,View-Based Recognition of Faces in Man and Machine: Re-visiting Inter-Extra-Ortho,Lecture Notes in Computer Science, 2525, pp. 651-660, 2002.
Wiskott, L. et al.,Face Recognition by Elastic Bunch Graph Matching,IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), pp. 775-779, 1997.
Heisele, B., Component-based Object Recognition, Designing Tomorrow's Category-Level 3D Object Recognition Systems: An International Workshop, Taormina, Sicily, 2003.
Clippingdale, S. et al., “Performance Improvement and Database Registration in the Favret Face Detection, Tracking and Recognition System,” Technical Report of the Institute of Electronics Information and Communication Engineers, Mar. 8, 2001, vol. 10, No. 701, pp. 111-118, Japan. (with English abstract).
Japanese Office Action, Japanese Patent Application No. 2006-516619, Feb. 16, 2010, 9 pages.
Bella Matthew C
Duell Mark E.
Fenwick & West LLP
Honda Motor Co. Ltd.
Rosario Dennis
LandOfFree
Systems and methods for training component-based object... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Systems and methods for training component-based object..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Systems and methods for training component-based object... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4159169