Image analysis – Applications – Personnel identification
Reexamination Certificate
2005-09-27
2005-09-27
Mariam, Daniel (Department: 2621)
Image analysis
Applications
Personnel identification
C382S159000
Reexamination Certificate
active
06950538
ABSTRACT:
A method of authenticating a signature including the steps of sampling a signature and storing data representative of the signature, converting the data to high dimensions vectors, feeding the high dimension vectors to an unsupervised neural network, performing a high order principal component extraction process on the high dimensions vectors to thereby identifying clusters of high dimension points, and analyzing the clusters of high dimension points to determine, based on previously stored information, the authenticity of the signature. Also an apparatus for such authentication including a sampling device for sampling a signature and storing data representative of the signature, a converting device connected downstream of the sampling device for converting the data to high dimension vectors, an unsupervised neural network for receiving the high dimension and performing a high order principal component extraction process on the high dimensions vectors to thereby identify clusters of high dimension points, and an analyzing device connected to the unsupervised neural network for analyzing the clusters of high dimension points to determine the authenticity of the signature.
REFERENCES:
patent: 4028674 (1977-06-01), Chuang
patent: 5046019 (1991-09-01), Basehore
patent: 5164992 (1992-11-01), Turk et al.
patent: 5384895 (1995-01-01), Rogers et al.
patent: 5442715 (1995-08-01), Gaborski et al.
patent: 5465308 (1995-11-01), Hutcheson et al.
patent: 5553156 (1996-09-01), Obata et al.
patent: 5559895 (1996-09-01), Lee et al.
patent: 5568591 (1996-10-01), Minot et al.
patent: 5636291 (1997-06-01), Bellegarda et al.
patent: 5680470 (1997-10-01), Moussa et al.
patent: 5742702 (1998-04-01), Oki
patent: 5745598 (1998-04-01), Shaw et al.
patent: 5774571 (1998-06-01), Marshall
patent: 5812698 (1998-09-01), Platt et al.
patent: 5825906 (1998-10-01), Obata et al.
patent: 5828772 (1998-10-01), Kashi et al.
patent: 5987232 (1999-11-01), Tabuki
patent: 5995953 (1999-11-01), Rindtorff et al.
patent: 6084985 (2000-07-01), Dolfing et al.
Tayel, M. et al., Winner-Take-All Neural Network for Visual Handwritten Character Recognition, Proceedings of the Thirteenth National Radio Science Conference, Mar. 1996, pp. C4, 1-11.
Siemens, A.G., Handwritten Digit Recogniton with Principal Component Analysis and Radial Basis Functions, IEEE publications, 1993 International Joint Conference on Neural Networks, pp. 2253-2256.
Hyman, S.D. et al., Classification of Japanese Kanji Using Principal Component Analysis as a Preprocessor to an Artificial Neural Network, IEEE publication, 1991, pp. 233-238.
Mao, J. et al., A Self-Organizing Network for Hyperellipsoidal Clustering (HEC), IEEE Transactions on Neural Networks, vol. 7, No. 1, Jan. 1996, pp. 16-29.
Chang, Hong-De et al., Dynamic Handwritten Chinese Signature Verification, IEEE publication, Jul., 1993, pp. 258-261.
Tattersall et al., “Packed Hyper-Ellipsoid Classifiers”, Electronics Letters, Mar. 3rd,1994, vol. 30, No. 5, p. 427.
Transmittal and Supplementary European Search Report for Application No. 00900301.3, 2 pages, Mar. 1, 2004.
Avni Yossi
Suchard Eytan
Computer Associates Think Inc.
Fish & Richardson P.C.
Mariam Daniel
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