Abstract
In this paper, modified corner curve features based off-line signature recognition method proposed for Uyghur handwritten signature. The signature images were preprocessed according to the nature of Uyghur signature. Then corner curve features (CCF) and modified corner curve features (MCCF) with different 3 dimensional vectors were extracted respectively. Experiments were performed using Euclidean distance classifier, and non-linear SVM classifier for Uyghur signature samples from 50 different people with 1000 signatures, two kinds of experiments were performed for and variations in the number of training and testing datasets, and a high recognition rate of 98.9 % was achieved with MCCF-16. The experimental results indicated that modified corner curve features can efficiently capture the writing style of Uyghur signature.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61163028, 61563052), College Scientific Research Plan Project of Xinjiang Uyghur Autonomous Region (No. XJEDU2013I11), and Special Training Plan Project of Xinjiang Uyghur Autonomous Region’s Minority Science and Technological Talents (No. 201323121).
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Ubul, K., Abudurexiti, R., Mamat, H., Yadikar, N., Yibulayin, T. (2016). Uyghur Off-line Signature Recognition Based on Modified Corner Curve Features. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_46
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DOI: https://doi.org/10.1007/978-3-319-46654-5_46
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