Image analysis – Learning systems – Trainable classifiers or pattern recognizers
Reexamination Certificate
2005-03-10
2009-02-17
Chen, Wenpeng (Department: 2624)
Image analysis
Learning systems
Trainable classifiers or pattern recognizers
C382S115000, C382S224000
Reexamination Certificate
active
07492943
ABSTRACT:
An open set recognition system utilizing transductive inference including capture device(s), a basis, quality checker(s), feature extractor(s), a gallery, a rejection threshold, a storage mechanism, and a recognition stage. The basis encodes sample(s) and is derived using training samples. The feature extractor(s) generates signature(s) from sample(s) using the basis. The rejection threshold is created using a rejection threshold learning mechanism that calculates the rejection threshold using sample(s) by: swapping a sample identifier with other sample identifier(s); computing a credibility value for the swapped sample identifiers; deriving a peak-to-side ratio distribution using the credibility values; and determining the rejection threshold using the peak-to-side ratio distribution. The open set recognition stage authenticates or reject as unknown the identity of unknown sample(s) using derived credibility values, derived peak-to-side ratios for the unknown sample and the rejection threshold.
REFERENCES:
patent: 3902160 (1975-08-01), Kawa
patent: 5271088 (1993-12-01), Bahler
patent: 6246794 (2001-06-01), Kagehiro et al.
patent: 7072873 (2006-07-01), Gammerman et al.
patent: 09128487 (1997-05-01), None
“Open set text-independent speaker recognition based on set-score pattern claasification” Jiuqing Deng; Qixiu Hu; Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03) Conference on vol. 2, Apr. 6-10, 2003 pp. II—73-76 vol. 2.
“Transductive confidence machine for active learning” Shen-Shyang Ho; Wechsler, H.; Neural Networks, 2003. Proceedings of the International Joint Conference on vol. 2, Jul. 20-24, 2003 pp. 1435-1440 vol. 2.
Automatic Determination of Reject Thresholds in Classifiers Employing Discriminant Functions, H. C. Yau and M. T. Manry, IEEE Transactions on Signal Processing. vol. 40. No. 3 Mar. 1992, pp. 711-713.
“To Reject or Not to Reject: That is the Question—An Answer in Case of Neural Classifiers”, Claudio De Stefano, Carlo Sansone, and Mario Vento, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, vol. 30, No. 1, Feb. 2000, pp. 84-94.
“Moderating the Outputs of Support Vector Machine Classifiers”, James Tin-Yau Kwok, IEEE Transactions on Neural Networks, vol. 10, No. 5, Sep. 1999, 1018-1031.
E. Bailly-Bailliere et al. (2003), The BANCA Database and Evaluation Protocol, 4th Audio-Video Based Person Authentication (AVBPA), 625-638.
S. Bengio, J. Mariethoz, and S. Marcel (2001), Evaluation of Biometric Technology on XM2VTS, IDIAP-RR 01-21, European Project BANCA Deliverable D71, Martigny, Switzerland.
A. Blum and T. Mitchell (1998), Combining Labeled and Unlabeled Data with Co-Training, in COLT: Proc. of the Workshop on Computational Learning Theory, Morgan Kaufmann, 92-100.
R. Chellappa, C. L. Wilson and S. Sirohey (1995), Human and Machine Recognition of Faces: A Survey, Proc. IEEE, vol. 83, No. 5, 705-740.
T. M. Cover and P. E. Hart (1967), Nearest neighbor pattern classification, IEEE Trans. Inform. Theory IT 13:21-7.
P. Grother (2004), Face Recognition Vendor Test 2002, Supplemental Report—NISTIR 7083.
J. Daugman (1997), Face and Gesture Recognition: Overview, IEEE Trans. on PAMI, vol. 19, 7, 675-676.
G. R. Doddington, W. Liggett, A. Martin, M. Przybocki and D. Reynolds (1998), Sheep, Goats, Lambs and Wolves: A Statistical Analysis of Speaker Performance, Proc. Of IC-SLD'98, 1351-1354.
S. Furui (1997), Recent Advances in Speaker Recognition, Pattern Recognition Letters, 18, 859-872.
A. Gammerman, V. Vovk, and V. Vapnik (1998), Learning by Transduction. In Uncertainty in Artificial Intelligence, 148-155.
A. Jain and A. Ross (2004), Multibiometric Systems, Comm. of ACM, vol. 47, No. 1,34-40.
T. Joachims (1999), Transductive Inference for Text Classification Using Support Vector Machines, in I. Bratko and S. Dzeroski (eds.), Proc. Of ICML-99, 16th Int. Conf. on Machine Learning, Bled, Slovenia, Morgan Kaufmann, 200-209.
P. Juszczak and R. P. W. Duin (2004), Selective Sampling Based on the Variation in Label Assignment, 17th Int. Conf. on Pattern Recognition (ICPR), Cambridge, England.
M. A. Krogel and T. Scheffer (2004), Multi-Relational Learning, Text Mining, and Semi-Supervised Learning from Functional Genomics, Machine Learning, 57, 61-81.
M. Kukar and I. kononenko (2002), Relaible Classifications with Machine Learning, 13th European Conf. on Machine Learning (ECML), Helsinki, Finland.
C. Liu and H. Wechsler (2002), Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition, IEEE Trans. on Image Processing, vol. 11, No. 4, 467-476.
M.W. Mak, W.D. Zhang and M.X. He (2001), A New Two-Stage Scoring Normalization Approach to Speaker Verification, Proc. Int. Sym. on Intelligent Multimedia, Video and Speech Processing, pp. 107-110, Hong Kong.
T. Melluish, C. Suanders, I. Nouretdinov, I and V. Vovk (2001), The Typicalness Framework: A Comparison with the Bayesian Approach. TR, Dept. of Computer Science, Royal Holloway, University of London, http://www.clrc.rhul.ac.uk/tech-report/.
T. Mitchell (1999), The Role of Unlabelled Data in Supervised Learning, Proc. 6th Int. Colloquium on Cognitive Sciences, San Sebastian, Spain.
K. Nigam, A. K. McCallum, S. Thrun and T. M. Mitchell (2000), Text Classification from Labeled and Unlabeled Documents Using EM, Machine Learning, 39 (2/3), 103-134.
S. Pankanti, N. K. Ratha and R. M. Bolle (2002), Structure in Errors: A Case Study in Fingerprint Verification, 16th Int. Conf. on Pattern Recognition, Quebec-City, Canada.
P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss (1998), The FERET Database and Evaluation Procedure for Face Recognition Algorithms, Image and Vision Computing, vol. 16, No. 5, 295-306.
P. J. Phillips, P. Grother, R. J. Michaels, D. M. Blackburn, E. Tabassi and M. Bone (2003), Face Recognition Vendor Test 2002—Overview and Summary.
K. Proedrou, I. Nouretdinov, V. Vovk and A. Gammerman (2001), Transductive Confidence Machines for Pattern Recognition, TR CLRC-TR-01-02, Royal Holloway University of London.
D. A. Reynolds, T. F. Quatieri, and R. B. Dunn (2000), Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing 10, 19-41.
A. Ross and A. Jain (2004), Information fusion in Biometrics, Pattern Recognition Letters, vol. 24, 2115-2125.
C. Saunders, A. Gammerman, and V. Vovk (1999), Transduction with Confidence and Credibility, 16th Int. Joint Conf. on Artificial Intelligence (IJCAI), Stockholm, Sweden.
R. Snelick, M. Indovina, J. Yen, and A. Mink (2003), Multimodal Biometrics: Issues in Design and Testing, ICMI'03, Vancouver, BC, Canada.
S. Tong and D. Koller (2001), Support Vector Machines Active Learning with Applications to Text Classification, Journal of Machine Learning Research, vol. 2, 45-66.
V. Vovk, A. Gammerman and C. Saunders (1999), Machine-Learning Application of Algorithm Randomness, Proceedings of the1 6th International Conference on Machine Learning, Bled, Slovenia.
W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld (2003), Face Recognition: A Literature Survey, Computing Surveys, vol. 35, No. 4, 399-458.
Li Fayin
Wechsler Harry
Chen Wenpeng
George Mason Intellectual Properties, Inc.
Grossman Davis
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