Abstract
In this paper, a novel approach has been proposed for online signature verification based on recursive subset training. Our approach is based on estimating the Equal Error Rate (EER) of the entire system and then splitting the entire data set into two subsets based on the EER of the system. The two subsets includes writers whose individual EER is more than the EER of the system and writers whose EER is less than the EER of the system. This procedure is recursively repeated until writer level parameters are decided. Unlike other verification models where same features are used for all writers, our approach is based on identifying writer dependent features and also writer dependent thresholds. Initially, writer dependent features are selected using a suitable feature selection method. Signatures are clustered using Fuzzy C means and represented in the form of interval valued symbolic feature vector. Signature verification is done based on the selected representation and the EER of system is calculated. Once the EER of the system is estimated, our method is based on estimating the EER of individual writers and splitting the dataset into subsets and estimating the EER of each of the subset separately. This process of splitting the dataset into subset and treating each of the subsystem separately is repeated until the individual writer thresholds and features are identified. We conducted experiments on MCYT-DB1 to show the effectiveness of our novel approach.
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Guru, D.S., Manjunatha, K.S., Manjunath, S. (2013). Online Signature Verification Based on Recursive Subset Training. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_36
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DOI: https://doi.org/10.1007/978-3-319-03844-5_36
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