Image analysis – Applications – Personnel identification
Reexamination Certificate
2006-05-23
2006-05-23
Chen, Wenpeng (Department: 2625)
Image analysis
Applications
Personnel identification
C382S159000, C382S181000
Reexamination Certificate
active
07050607
ABSTRACT:
A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face
on-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.
REFERENCES:
patent: 5774591 (1998-06-01), Black et al.
patent: 6272231 (2001-08-01), Maurer et al.
patent: 6671391 (2003-12-01), Zhang et al.
patent: 6804391 (2004-10-01), Blake et al.
patent: 6944319 (2005-09-01), Huang et al.
patent: 2003/0123754 (2003-07-01), Toyama
Ng, J. and S. Gong, “Performance multi-view face detection and pose estimation using a composite support vector machine across the view sphere” Proc. IEEE International Workshop on Recognition, Analysis and Tracking of Faces and Gestures, pp. 14-21, 1999.
Li, Y. M., S.G. Gong, and H. Liddell “Support Vector regression and classification based multi-view face detection and recognition” Proc. IEEE Int. Conf. of Face and Gesture Recognition, pp. 300-305, Mar. 2000.
Rowley, H.A., S. Baluja and T. Kanade, “Neural network-based face detection”, in IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 20, No. 1, pp. 23-38, Jan. 1998.
Fleuret, F. and D. Geman. Coarse-to-fine face detection. International Journal of Computer Vision, 20:1157-1163, 2001.
Viola, P. and M. Jones, Robust real time object detection. In IEEE ICCV Workshop on Statistical and □□ Computational Theories of Vision, Vancouver, Canada, Jul. 13, 2001.
Amit, Y., D. German, and K. Wilder. “Joint induction of shape features and tree classifiers”. May 1996.
Bichsel, M. and A.P. Pentland. “Human face recognition and the face image set's topology”. CVGIP: Image Understanding, 59:254-261, 1994.
Bins, J. and B. Draper. “Feature selection from huge feature sets”. In Proceedings of IEEE International Conference on Computer Vision, pp. 159-165, Jul. 9-12, 2001.
Crow, F. “Summed-area tables for texture mapping”. In SIGGRAPH, vol. 18(3), pps. 207-212, 1984.
Hjelmas, Erik, B.K.L. “Face detection: A survey”. Computer Vision and Images Understanding, 3(3), Sep. 2001.
Feraud, J., O. Bernier, and M. Collobert. “A fast and accurate face detector for indexation of face images”. In Proc. Fourth IEEE Int. Conf on Automatic ƒAce and Gesture Recognition, Grenoble, 2000.
Fleuret, F. and D. Geman. Coarse-to-fine face detection. International Jounal of Computer Vision, 20:1157-1163, 2001.
Freund, Y. and R. Schapire. “A Decision-theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and system Sciences, 55(1):119-139, Aug. 1997.
Friedman, J., T. Hastie, and R. Tibshirani. “Additive logistic regression: a statistical view of boosting”. Technical report, Department of sTatistics, sequoia Hall, Stanford University, Jul. 1998.
Gong, S., S. McKenna, and J. J. Collins, An investigation into face pose distributions. In Proc. Int'l Conf. on Autom. Face and Gesture Recog., pp. 265-270, 1996.
Huang, J., S. Shao, and H. Wechsler. “Face pose discrimination using support vector machines (SVM)”. In Proceedings of International Conference Pattern recognition, Brisbane, Queensland, Australia, 1998.
Jain, A. and D. Zongker. Feature selection: evaluation, application, and small sample performance. IEEE Trans. on PAMI, 19(2):153-158, 1997.
John, G.H., R. Kohavi, and K. Peger. “Irrelevant features and the subset selection problem”. In Processing of the Eleventh International Conference on Machine Learning, pp. 121-129, 1994.
Kuchinsky, A., C. Pering, M. L. Creech, D. Freeze, B. Serra and J. Gwizdka. Fotofile: A Consumer Multimedia Organization and Retrieval System. In Proc. ACM HCI'99 Conference, 1999.
Li, Y. M., S.G. Gong, and H. Liddell. “Support vector regression and classification based multi-view face detection and recognition”. In IEEE Int. Conf. of Face & Gesture Recognition, pp. 300-305, France, Mar. 2000.
Ng, J. and S. Gong, “Performing multi-view face detection and pose estimation using a composite support vector machine across the view sphere”. In Proc. IEEE International Workshop on Recognition, Analysis, and Tracking of Faces and gestures in Real-Time Systems, pp. 14-21, Corfu, Greece, Sep. 1999.
Osuna, E. R. Freund, and F. Girosi. “Training support vector machines: An application to face detection”. In CVPR, pp. 130-136, 1997.
Papageorgiou, C.P., M. Oren, and T. Poggio. “A general framework for object detection”. In Proceedings of IEEE International Conference on Computer Vision, pp. 555-562, Bombay, India, 1998.
Pentland, A., B. Moghaddam, and T. Starner, “View-Based and Modular Eigenspaces for Face Recognition,” In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 84-91, 1994.
Pudill, P. J. Novovicova, and J. Kittler. Floating search methods in feature selection with Nonmonotonic Criterion Functions. Department of Electronic & Electrical Engineering, University of Surrey Guildford, UK, 1994.
Roth, D., M. Yang, and N. Ahuja. “A snow-based face detector”. In Proceedings of Neural Information Processing Systems, 2000.
Rowley, H.A., S. Baluja and T. Kanade, “Neural network-based face detection”, in IEEE Transactions on Pattern Analysis and Machine Intellingence, vol. 20, No. 1, pp. 23-38, Jan. 1998.
Schapire, R., Y. Freund, P. Bartlett, and W. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. In Proc. 145h International Conference on Machine Learning, pp. 322-330. Morgan Kaufmann, 1997.
Schapire, R.E. and Y. Singer. Improved boosting algorithms using confidence-rated predictions. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80-91, 1998.
Schneiderman, H. and T. Kanade. “A Statistical method for 3d object detection applied to faces and cars”. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000.
Simard, P.Y., L. Bottou, P. Haffner, and Y. L. Cun. “Boxlets: a fast convolution algorithm for signal processing and neural networks”. In M. Kearns, S. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems, vol. 11, pp. 571-577, MIT Press, 1998.
Simard, P.Y., Y.A.L. Cun, J.S. Denker, and B. Victorri. “Transformation invariance in pattern recognition-tangent distance and tangent propagation”. In G.B. Orr and K.-R. Muller, editors, Neural Networks: Tricks of the trade. Springer, 1998.
Somol, .P.,P. Pudil, J. Novoviova, and P.Paclik. “Adaptive floating search methods in feature selection”. Pattern Recognition Letters, 20:1157-1163, 1999.
Sung, K., and T. Poggio, “Example-based Learning for View-Based Human Face Detection”, in IEEE Transactions on Pattern Analysis and Machine Intellingence, vol. 20, No. 1, pp. 39-51, Jan. 1998.
Tieu, K. and P. Viola. Boosting image retrieval. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1:pp. 228-235, 2000.
Viola, P. and M. Jones. Robust real time object detection. In IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, Jul. 13, 2001.
Wiskott, L., J. Fellous, N. Kruger, and C.V. Malsburg. “
Li ZiQing
Zhang Hong-Jiang
Zhang ZhenQiu
Chen Wenpeng
Lyon Katrina A.
Lyon & Harr LLP
Microsoft Corp.
Shah Utpal
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