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On-line signature recognition method based on linear regression

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Nowadays, automatic signature verification is an active area of researches in numerous applications such as bank check verification, access restriction or special areas such as police investigations. In our researches signature was captured by Topaz SigLite T-LBK750-HSB device, where some dynamic features of signature can be also registered. In many transactions, the electronic verification of a person's identity is beneficial, hence it inspires the development of a wide range of automatic identification systems. In this paper the system that automatically authenticates documents based on the owner's handwritten signature is presented.
Rocznik
Tom
Strony
97--104
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
autor
  • University of Silesia. Institute of Informatics, 41-200 Sosnowiec, Będzińska 39, Poland
autor
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] 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.
  • [4] 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.
  • [5] 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.
  • [6] ZHANG B., FU. M, YAN H., Handwritten signature verification based on neural gas based vector quantization. Proc. of 14th Int. Conf. on Pattern Recognition. Vol. 2, pp. 1862 – 1864, 1998.
  • [7] KORONACKI J., ĆWIK J., Statystyczne systemy uczące się. Wydawnictwa Naukowo-Techniczne, Warszawa, 2005.
  • [8] 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.
  • [9] ADAMSKI M., SAEED K., Signature image recognition by shape context image matching [appear in proceedings of XII Int. Conf. Medical Informatics and Technologies, Osieczany near Cracow, Poland, 2007].
  • [10] 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.
  • [11] 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.
  • [12] 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.
  • [13] TANABE K., YOSHIHARA M., KAMEYA S., MORI S., OMATA S., ITO T., Automatic signature verification based on the dynamic feature of pressure, Document Analysis and Recognition, 2001. Proc. of the sixth Int. Conf. on Volume , Issue , pp. 1045 – 1049, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0007-0009
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