NDLI: Signature Identification Using Dynamic and HMM Features and KNN Classifier

Content Provider	IEEE Xplore Digital Library
Author	Tahmasebi, A. Pourghassem, H.
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. Eng., Islamic Azad Univ., Najafabad, Iran (Tahmasebi, A.; Pourghassem, H.)
Abstract	Dynamic feature-based signature recognition systems obtain more identification accuracy rate than static feature-based signature recognition systems. On the other hand, because of point to point comparing of captured signals, local features produce less error than general features. In this paper, a novel signature identification algorithm based on dynamic and Hidden Markov Models (HMM) features and K-Nearest Neighbor (KNN) classifier is proposed. In this algorithm, special point dynamic features of handwritten signatures are selected and normalized and then used as signature features. In the used HMM, different number of states in transition matrix for each signature are calculated and used as feature vector. This strategy not only reduces complexity computational but also is extracted features based on general characteristics of signature. The proposed algorithm is evaluated on the standard SVC2004 database. The obtained results are reported based on various values of parameters of the proposed algorithm and compared with presented approaches in the literature.
Sponsorship	IEEE India Council MTT Chapt.
Starting Page	201
Ending Page	205
File Size	283356
Page Count	5
File Format	PDF
ISBN	9781467356039
e-ISBN	9781467356039
DOI	10.1109/CSNT.2013.51
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov Model features Handwriting recognition Databases Heuristic algorithms Hidden Markov models Support vector machine classification Dynamic features K-nearest neighbor Feature extraction Signature identification Classification algorithms
Content Type	Text
Resource Type	Article

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