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Determining signatures' characteristic features using statistical methods

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Many signatures verification systems have been developed so far. Most of them have some common algorithms and solutions. The problem is that authors of the solutions usually present the final results of a working system. They do not reveal effects of the particular components. This is why other researchers do not know which element improves the results and is worth using. This paper shows how to estimate, in an easy way, if the selected component/set of data/feature gives good results.
Rocznik
Tom
Strony
41--49
Opis fizyczny
Bibliogr. 16 poz., tab.
Twórcy
autor
  • University of Silesia. Institute of Informatics, 41-200 Sosnowiec, Będzińska 39, Poland
autor
Bibliografia
  • [1] SALWADOR S., CHAN P., FastDTW: Toward Accurate Dynamic Warping in Linear Time and Space. Proc. of the Int. Conf. on knowledge discovery and data mining – KDD’04, Seattle, USA, pp. 70–80, 2004.
  • [2] LEI H, et all., ER2: an Intuitive Similarity Measure for On-line Signature Verification. 9th Int. Workshop on Frontiers in Handwriting Recognition – IWFHR’04, Tokyo, Japan, pp. 191–195, 2004.
  • [3] FANG B, et all., Off-line signature verification by the tracking of feature and stroke positions. Pattern Recognition. Vol. 36, pp. 91–101., 2003
  • [4] PORWIK P., PARA T., Some Handwritten Signature Parameters in Biometric Recognition Process. Proc. of the 29th Int. Conf. on Information Technology Interfaces – ITI’07, pp. 185–190. Cavtat, Croatia, 2007.
  • [5] PORWIK P., The compact three stages method of the signature recognition. Proc of the 6th Int. IEEE Conf. Computer Systems and Industrial Management Applications, CISIM 2007. Ełk, pp. 282–287, 2007.
  • [6] RIOJA F. et all. Dynamics features Extraction for on-Line Signature verification. Proc o f the 14th IEE Int. Conf. on Electronics, Communications and Computers, Veracruz, Mexico, pp.156–162., 2004
  • [7] COETZER J., et all. Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model. EURASIP J. on Applied Signal Processing 2004, pp. 559–57, 2004.
  • [8] KRYSICKI W., BARTOS J., Rachunek prawdopodobieństwa i statystyka matematyczna. Wydawnictwo Naukowe PWN, Warszawa, 1986.
  • [9] SAEED K., ADAMSKI M., Extraction of Global Features for Offline Signature Recognition. Image Analysis, Computer Graphics, Security Systems and Artificial Intelligence Applications, WSFiZ Press, pp. 429–436, 2005.
  • [10] ADAMSKI M., SAEED K., Signature image recognition by shape context image matching [appear in proceeding s of XII Int. Conf. Medical Informatics and Technologies, Osieczany near Cracow, Poland, 2007].
  • [11] ADAMSKI M., SAEED K., Signature identification by view-based feature extraction and Dynamic Time Warping classifier. Proc. of the 13th Int. MultiConf. on Advanced Computer Systems–ACS–AIBITS/CISIM'06, Miedzyzdroje, Poland, pp. 67–74, 2006.
  • [12] LEE L., BERGER T., AVICZER E., Reliable On-Line Human Signature Verification Systems. IEEE Trans. on Pattern Analysis and Machine Intelligence , pp. 643–647, 1996.
  • [13] RHEE T., CHO S., KIM J., On-Line Signature Verification Using Model-Guided Segmentation and Discriminative Feature Selection for Skilled Forgeries. The 6th International Conference on Document Analysis and Recognition (ICDAR), 2001.
  • [14] DOROZ R., PORWIK P., PARA T., WRÓBEL K. Dynamic Signature recognition based on velocity changes of some features. International Journal of Biometrics, Vol.1 no.1 , pp. 47 – 62, 2008.
  • [15] Signatures database: http://www.cse.ust.hk/svc2004/index.html
  • [16] Software for automatic testing http://www.autoitscript.com/autoit3/
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0006-0005
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