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A Metric Learning Approach for Offline Writer Independent Signature Verification

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Abstract

An efficient offline system for writer independent signature verification is proposed in this work which is quite a difficult task in computer vision. The proposed system employs a novel global representation of signatures followed by the Mahalanobis distance based dissimilarity score to discriminate between the original signatures and their skilled forgeries. The global representation of an image containing signature is based on aggregation of local descriptors using a vocabulary. The global descriptors from the pair of images are then used to learn a low-rank distance metric which is not a trivial task owing to the high dimensionality of the descriptor. The experimental results are reported on two datasets namely CEDAR and BHSig260; both containing a sufficient number of writers and are used as benchmark datasets. A comparison with the state-of-the-art approaches demonstrates the efficiency of our proposed approach. Our proposed method achieved 100% accuracy on CEDAR dataset and outperformed all other methods on BHSig260 (Bengali) dataset. The results on BHSig260 (Hindi) dataset are also promising.

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Notes

  1. http://www.vlfeat.org

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ACKNOWLEDGMENTS

The authors would like to acknowledge the support of Center of Excellence in Intelligent Engineering Systems (CEIES) and Department of Electrical and Computer Engineering at King Adulaziz University for providing the necessary computational resources for this work.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, 21589, Jeddah, Saudi Arabia

    Muhammad Shehzad Hanif & Muhammad Bilal

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  1. Muhammad Shehzad Hanif
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  2. Muhammad Bilal
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Correspondence to Muhammad Shehzad Hanif or Muhammad Bilal.

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The authors declare that they have no conflict of interest.

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Muhammad Shahzad Hanif received his B.Sc. Electrical Engineering in 2001 from University of Engineering and Technology, Lahore, Pakistan, M.S. in Engineering Sciences in 2006 and Ph.D. in Computer Engineering in 2009 from University Pierre and Marie Curie, France. Currently, he is an Associate Professor at the Department of Electrical and Computer, King Abdulaziz University, Jeddah, Saudi Arabia. His research interests include machine learning, image analysis, information fusion, and object detection and tracking.

Muhammad Bilal received B.S. Electronics Engineering from G.I.K Institute of Engineering Sciences and Technology, Pakistan in 2002 and M.S. Computer Engineering and Ph.D. Electrical Engineering from Lahore University of Management Sciences, Pakistan in 2007 and 2013, respectively. He is working as Associate Professor in Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia since 2014 with joint appointment in Center of Excellence in Intelligent Engineering Systems (CEIES). Earlier he worked at Center for Integrated Smart Sensors (CISS) at KAIST, South Korea as a post-doctoral researcher. His research interests include system design for image and video processing applications, approximate arithmetic circuits, and embedded systems.

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Hanif, M.S., Bilal, M. A Metric Learning Approach for Offline Writer Independent Signature Verification. Pattern Recognit. Image Anal. 30, 795–804 (2020). https://doi.org/10.1134/S1054661820040173

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