Abstract
In this paper, we present a local morphological pattern spectrum based approach for off-line signature verification. The proposed approach has three major phases : Preprocessing, Feature extraction and Classification. In the feature extraction phase, the signature image is partitioned into eight equally sized vertical blocks and local morphological pattern spectra of each block is obtained. The spectrum thus obtained for each block is converted to normalised ten bin histogram and to form a feature vector of the signature. The Earth Movers Distance (EMD) measure is used for classification and the performance is measured through FAR/FRR metric. Experiments have been conducted on standard signature datasets namely CEDAR and GPDS-160, and MUKOS, a regional language (Kannada) dataset. The comparative study is also provided with the well known approaches to exhibit the performance of the proposed approach.
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Shekar, B.H., Bharathi, R.K., Pilar, B. (2013). Local Morphological Pattern Spectrum Based Approach for Off-line Signature Verification. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_45
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DOI: https://doi.org/10.1007/978-3-642-45062-4_45
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