Method and system of transitive matching for object...

Image analysis – Histogram processing – For setting a threshold

Reexamination Certificate

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C340S005830

Reexamination Certificate

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06920231

ABSTRACT:
A method and system of transitivity matching suitable for object recognition, and in particular biometric recognition such as face or fingerprint recognition, is provided. The invention provides a means for pre-encoding object match information from each set of raw object sample data or scores from an underlying recognition algorithm. A sample space is constructed and the raw scores are mapped into that space. Preferably, a recognition space is also constructed and the same space scores are further mapped into the recognition space. Each space may be truncated to remove dispensable modes of that space. The distance between object data samples encoded in the sample space or the recognition space can be more rapidly determined, and the encoded data samples are also significantly compressed, compared to the raw scores.

REFERENCES:
Daniel L. Swets et al., “Using Discriminant Eigenfeatures for Image retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, No. 8, pp 831-838, Aug. 1996.
Baback Moghaddam et al., “Probabilistic Visual Learning for Object Representation”, IEEE Transactions on Pattern Analysis an Machine Intelligence, vol. 19, No. 7, pp 696-710, Jul. 1997.
P. Jonathan Phillips; “Support Vector Machines Applied to Face Recognition”;Advances in Neural Information Processing Systems 11, M.J. Keanrs, S.A. Solla, and D. A. Chon, MIT Press, 1999.
Penev et al.; Local Feature Analysis: A General Statistical Theory for Object Representation;Network: Computation in Neural Systems 7(1996), 477-500.
Lades et al.; “Distortion Invariant Object Recognition in the Dynamic Link Architecture”;IEEE Transactions on Computers, (Mar. 1993).
Turk et al; “Eigenfaces for Recognition”;Journal of Cognitive Neuroscience, vol. 3, No. 1; (1991), 72-86.
Moghaddam et al.; “Beyond Eigenfaces: Probablistic Matching for Face Recognition”;The 3rdInt'l Conference on Automatic Face&Gesture Recognition, Nara, Japan, (Apr. 1998, 1-6.

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