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Method of signature recognition with the use of the complex features

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper a new method of handwritten signatures verification has been proposed. This method, for each signature, creates complex features which are describing this signature. These features are based on dependencies analysis between dynamic features registered by tablets. These complex features are then used to create vectors describing the signature. Elements of these vectors are calculated using measures proposed in this work. The similarity between signatures is assessed by determining the similarity of vectors in the compared signatures. Research, whose results will be presented in the further part of this work, have shown a high efficiency of verification using proposed method.
Rocznik
Tom
Strony
155--162
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr.
Twórcy
autor
  • University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland
autor
  • University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland
autor
  • University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland
autor
  • University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland
Bibliografia
  • [1] BERTHOLD M. R., CEBRON N., DILL F., GABRIEL T. R., et al., KNIME: The Konstanz Information Miner. In: Studies in Classification, Data Analysis, and Knowledge Organization, Springer, 2007, pp. 319-326.
  • [2] BREIMAN L., Random forests, Machine Learning, 2001, Vol. 45, pp. 5-32.
  • [3] DEMSAR J., Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 2006, Vol. 7, pp. 1-30.
  • [4] FANK F., GENERATING I. H., Accurate Rule Sets Without Global Optimization, In: 15th Int. Conf. on Machine Learning, 1998, pp. 144-151.
  • [5] FAUNDEZ-ZANUY M., On-line signature recognition based onVQ-DTW, Pattern Recognition 40, 2007, pp. 981-992.
  • [6] FIERREZ-AGUILAR J., NANNI L., LOPEZ-PENALBA J., ORTEGA-GARCIA J., MALTONI D., An on-line signature verification system based on fusion of local and global information, The AVBPA05 Proceedings of the 5th international conference on audio and video-based biometric person authentication, 2005, pp. 523-532.
  • [7] HALL M., FRANK E., HOLMES G., PFAHRINGER B., REUTEMANN P., WITTEN I. H., The WEKA Data Mining Software: An Update. SIGKDD Explorations, 2009, Vol. 11, No. 1, pp. 10-18.
  • [8] HARDLE W., SIMAR L., Applied multivariate statistical analysis, Springer-Verlag, Berlin-Heidelberg-New York, 2003.
  • [9] IMPEDOVO S., PIRLO G., Verification Of Handwritten Signatures An Overview, 14th International Conference on Image Analysis and Processing - ICIAP, Modena, Italy, 2007, pp. 191-196.
  • [10] KUDLACIK P., PORWIK P., A new approach to signature recognition using the fuzzy method, Pattern Analysis and Applications, 2014, Vol. 17, No. 3, pp. 451-463.
  • [11] PALYS M., DOROZ R., PORWIK P., Statistical analysis in signature recognition system based on Levenshtein distance, The 8 Int. Conf. on Computer Recognition Systems, CORES, 2013, Poland, pp. 217-226.
  • [12] PALYS M., DOROZ R., PORWIK P., On-line signature recognition based on an analysis of dynamic feature, In: IEEE Int. Conf. on Biometrics and Kansei Engineering (ICBAKE 2013), Tokyo Metropolitan University Akihabara, 2013, pp. 103-107.
  • [13] PLAMONDON R., SRIHARI S. N., On-line and off-line handwriting recognition: A comprehensive survey, In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, Vol. 22, No. 1, pp. 63-84.
  • [14] RUUD M. B., CONNEL J. H., PANKANTI S., RATHA N. K., SENIOR A. W., Guide to biometrics, Springer, 2008.
  • [15] SHAKHNAROVICH G., DARRELL T., INDYK P., Nearest-neighbor methods in learning and vision: theory andpractice, Neural Information, Processing, The MIT Press, 2006.
  • [16] SPECHT D. F., Probabilistic Neural Networks, Neural Networks, 1990, Vol.3, pp. 109-118.
  • [17] TAVENARD R., AMSALEG L., Improving the Efficiency of Traditional DTW Accelerators, Soumis a Knowledge and Information Systems (KAIS), Rennes, France, 2012.
  • [18] VAN B. L., GARCIA-SALICETTI S., DORIZZI B., On using the Viterbi path along with HMM likelihood information for online signature verification, In: IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 2007, Vol. 37, No. 5, pp. 1237-1247.
  • [19] VILLAVERDE K., KREINOVICH V., A linear-time algorithm that locates local extrema of a function of one variable from interval measurement results, Interval Computations, 1993, pp.176-194.
  • [20] WROBEL K., DOROZ, R., Method of Signature Recognition with the Use of the Mean Differences, Proceedings of the 31st International IEEE Conference on Information Technology Interfaces (ITI 2009), Croatia, 2009, pp. 231-235.
  • [21] http://atvs.ii.uam.es/databases.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e10b5e62-ec2a-4500-a1a7-2ae25898a0e5
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