Presentation 2008-02-21
Personal Authentication by Handwritten Characters Using Sequential Neural Network
Hideaki MATSUMOTO, Michio UMEDA,
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Abstract(in English) This paper proposes a personal authentication method by writer verification using a sequential neural network which can reduce a sense of rejection and resistance problem exists in biometric authentication. The sequential neural network that can handle time series patterns applied to the field of offline writer verification is almost no example. This method can obtain the features such as character types, writing locus and input order of characters by using multiple handwritten characters treated as time series data. From the evaluation experiment at the three cases of illegal authentication, it is shown that the proposed personal authentication method is effective.
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Keyword(in English) personal authentication / writer verification / handwritten characters / weighted direction index histogram method / sequential neural network
Paper # PRMU2007-222
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Conference Information
Committee PRMU
Conference Date 2008/2/14(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Personal Authentication by Handwritten Characters Using Sequential Neural Network
Sub Title (in English)
Keyword(1) personal authentication
Keyword(2) writer verification
Keyword(3) handwritten characters
Keyword(4) weighted direction index histogram method
Keyword(5) sequential neural network
1st Author's Name Hideaki MATSUMOTO
1st Author's Affiliation Graduate School of Engineering, Osaka Electro-Communication University()
2nd Author's Name Michio UMEDA
2nd Author's Affiliation Graduate School of Engineering, Osaka Electro-Communication University
Date 2008-02-21
Paper # PRMU2007-222
Volume (vol) vol.107
Number (no) 491
Page pp.pp.-
#Pages 6
Date of Issue