Pattern detection methods and systems and face detection...

Image analysis – Learning systems – Trainable classifiers or pattern recognizers

Reexamination Certificate

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C706S045000

Reexamination Certificate

active

07099504

ABSTRACT:
Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.

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Burges, “Simplified Support Vector Decision Rules”, Bell Laboratories, Lucent Technologies, 7 pages.
Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Microsoft Research Technical Report, Apr. 21, 1998, 21 pages.
Rowley et al., “Neural Network-Based Face Detection”, IEEE 1996, pp. 203-208.
Rowley et al., “Neural Network-Based Face Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, No. 1, Jan. 1998, pp. 23-38.
Sung, “Example-Based Learning for View-Based Human Face Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, No. 1, Jan. 1998, pp. 39-51.
B.Scholkopf, C. Burges, A. Smola, “Advances in Kernel Methods: Support Vector Learning,” MIT Press, 1999.
V. Vapkin, “The Nature of Statistical Learning Theory,” Springer-Verlag New York, Inc., 1995.
Osuna et al., “Training Support Vector Machines: an Application to Face Detection,” Proceedings of CVPR '97, Jun. 17-19, 1997, 8 pages.
Scholkopf et al., “Input Space Versus Feature Space in Kernel-Based Methods,” IEEE Transactions on Neural Networks, vol. 10, No. 5, Sep. 1999, pp. 1000-1017.
Burges, “Simplified Support Vector Decision Rules, ”Bell Laboratories, Lucent Technologies, 7 pages.
Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,” Microsoft Research Technical Report, Apr. 21, 1998, 21 pages.
Rowley et al., “Neural Network-Based Face Detection,” IEEE 1996, pp. 203-208.
Rowley et al., “Neural Network-Based Face Detection,” IEEE Transactions on Pattern Analysis Machine Intelligene, vol. 20, No. 1, Jan. 1998, pp. 23-38.
Sung, “Example-Based Learning for View-Based Human Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, No. 1, Jan. 1998, 39-51.
Christopeher J. C. Burges; A Tutorial on Support Vector Machines for Pattern Recognition; Kluwer Academic Publishers, Boston.; (43 pages).

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