DocumentCode
2262924
Title
A 1-D, sequence decomposition based, autoregressive hidden Markov model for dynamic signature identification and verification
Author
Paulik, Mark J. ; Mohankrishnan, N.
Author_Institution
Dept. of Electr. Eng., Univ. of Detroit Mercy, MI, USA
fYear
1993
fDate
16-18 Aug 1993
Firstpage
138
Abstract
A new model for use in writer identification and verification is presented. The signature, represented by a one-dimensional (1-D) spatial stochastic sequence, is decomposed into pseudo-stationary segments; a characterization which allows descriptions of abrupt and gradual changes in the contours. An autoregressive hidden Markov model is employed to describe the evolution of such changes. An experimental study is presented which demonstrates the model´s effectiveness
Keywords
autoregressive processes; handwriting recognition; hidden Markov models; pattern matching; 1D sequence decomposition based model; autoregressive hidden Markov model; dynamic signature identification; one-dimensional spatial stochastic sequence; pseudo-stationary segments; signature verification; writer identification; Credit cards; Databases; Fatigue; Handwriting recognition; Hidden Markov models; Marketing and sales; Muscles; Spatial resolution; Stochastic processes; Switches;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
Conference_Location
Detroit, MI
Print_ISBN
0-7803-1760-2
Type
conf
DOI
10.1109/MWSCAS.1993.343046
Filename
343046
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