Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/8600
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dc.contributor.authorPushpalatha, K.N.
dc.contributor.authorSupreeth, Prajwal, S.
dc.contributor.authorGautam, A.K.
dc.contributor.authorKumar, K.B.S.
dc.date.accessioned2020-03-30T10:22:27Z-
dc.date.available2020-03-30T10:22:27Z-
dc.date.issued2014
dc.identifier.citationInternational Conference on Recent Advances and Innovations in Engineering, ICRAIE 2014, 2014, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8600-
dc.description.abstractAutomatic offline signature verification and recognition is becoming essential in personal authentication. In this paper, we propose a transform domain offline signature verification system based on contourlet transform, directional features and Hidden Markov Model (HMM) as classifier. The signature image is preprocessed for noise removal and a two level contourlet transform is applied to get feature vector. The textural features are computed and concatenated with coefficients of contourlet transform to form the final feature vector. HTK tool with HMM classifier is used for classification. The parameters of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are calculated for GPDS-960 database. It is found that the parameters of FRR and FAR are improved compared to the existing algorithms. � 2014 IEEE.en_US
dc.titleOffline signature verification based on contourlet transform and textural features using HMMen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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