Proceedings of 36th Midwest Symposium on Circuits and Systems
DOI: 10.1109/mwscas.1993.343046
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A 1-D, sequence decomposition based, autoregressive hidden Markov model for dynamic signature identification and verification

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Cited by 13 publications
(5 citation statements)
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“…In [22], an autoregressive (AR) model is used in combination with a hidden Markov model (HMM) for writer identification. The signature, represented by a one-dimensional spatial stochastic sequence, is decomposed into pseudostationary segments.…”
Section: Digitizing Table-based Systemsmentioning
confidence: 99%
“…In [22], an autoregressive (AR) model is used in combination with a hidden Markov model (HMM) for writer identification. The signature, represented by a one-dimensional spatial stochastic sequence, is decomposed into pseudostationary segments.…”
Section: Digitizing Table-based Systemsmentioning
confidence: 99%
“…In [11], an autoregressive (AR) model is used in combination with a hidden Markov model (HMM) for writer identification. The signature, represented by a one-dimensional spatial stochastic sequence, is decomposed into pseudostationary segments.…”
Section: On-line Systemsmentioning
confidence: 99%
“…Handwritten signature verification has been extensively studied in the past decade [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Its applications include banking, credit card validation, and security systems.…”
Section: Introductionmentioning
confidence: 99%
“…In general, handwritten signature verification can be categorized into two kinds-online verification and offline verification. Online verification requires a stylus and an electronic tablet connected to a computer to grab dynamic signature information [10][11][12]16]. Offline verification, on the other hand, deals with signature information which is in a static format [13][14][15].…”
Section: Introductionmentioning
confidence: 99%