Abstract
Handwritten signature has been extensively adopted as biometric for identity verification in daily life, as it is the most widely accepted personal authentication method. Automatic signature recognition technologies can definitely facilitate the verification process. Many research attempts and advances have occurred in this field, automatic signature verification still is a challenging and important issue. This work presents a novel and robust on-line signature verification approach using Hidden Semi-Markov Model (HSMM). The proposed system comprises three stages. First, dynamic features are extracted according to the local statistical information of velocity, acceleration, azimuth, altitude, and pressure. Next, the extracted features are normalized into unified observation length. To improve the verification accuracy, features with slight variation are clustered into the same class using K-means classification algorithm. Furthermore, the Forward-Backward algorithm is utilized to accelerate the computation of HSMM parameters. Finally, the system builds a unique HSMM for each identity and estimates the signature baseline in corresponding to the features. To assess the recognition performance of the proposed algorithm, experiments were conducted using SVC2004 signature database. Analytical results reveal that the proposed method is very promising.
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Lin, DT., Liao, YC. (2011). On-line Handwritten Signature Verification Using Hidden Semi-Markov Model. In: Stephanidis, C. (eds) HCI International 2011 – Posters’ Extended Abstracts. HCI 2011. Communications in Computer and Information Science, vol 173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22098-2_117
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DOI: https://doi.org/10.1007/978-3-642-22098-2_117
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