Online handwritten signature recognition by length normalization using up-sampling and down-sampling
With the rapid advancement of capture devices like tablet or smart phone, there is a huge potential for online signature applications that are expected to occupy a large field of researches in forthcoming years. Online handwritten signature encounters difficulty in the verification process because a...
| Main Authors: | , , , , , |
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| Format: | Article |
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The Society of Digital Information and Wireless Communications
2015
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| _version_ | 1825948509794205696 |
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| author | Malallah, Fahad Layth Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Arigbabu, Olasimbo Ayodeji Iranmanesh, Vahab Yussof, Salman |
| author_facet | Malallah, Fahad Layth Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Arigbabu, Olasimbo Ayodeji Iranmanesh, Vahab Yussof, Salman |
| author_sort | Malallah, Fahad Layth |
| collection | UPM |
| description | With the rapid advancement of capture devices like tablet or smart phone, there is a huge potential for online signature applications that are expected to occupy a large field of researches in forthcoming years. Online handwritten signature encounters difficulty in the verification process because an individual rarely produce exactly the same signature whenever he signs. This difference in the produced signature is referred to as intra-user variability. Verification difficulty occurs especially in the case where the feature extraction and classification algorithms are designed to classify a stable length vector of input features. In this paper, we introduce an efficient algorithm for online signature length normalization by using Up-Sampling and Down-Sampling techniques. Furthermore, online signature verification system is also proposed by using both Principal Component Analysis (PCA) for feature extraction and Artificial Neural Network (ANN) for classification. The SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals, is used to evaluate the effectiveness of the proposed technique. Based on the tests performed, the proposed technique managed to achieve False Accept Rate (FAR) of 5.5% and False Reject Rate (FRR) of 8.75%. |
| first_indexed | 2024-03-06T08:29:55Z |
| format | Article |
| id | upm.eprints-34745 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T08:29:55Z |
| publishDate | 2015 |
| publisher | The Society of Digital Information and Wireless Communications |
| record_format | dspace |
| spelling | upm.eprints-347452015-12-21T13:59:51Z http://psasir.upm.edu.my/id/eprint/34745/ Online handwritten signature recognition by length normalization using up-sampling and down-sampling Malallah, Fahad Layth Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Arigbabu, Olasimbo Ayodeji Iranmanesh, Vahab Yussof, Salman With the rapid advancement of capture devices like tablet or smart phone, there is a huge potential for online signature applications that are expected to occupy a large field of researches in forthcoming years. Online handwritten signature encounters difficulty in the verification process because an individual rarely produce exactly the same signature whenever he signs. This difference in the produced signature is referred to as intra-user variability. Verification difficulty occurs especially in the case where the feature extraction and classification algorithms are designed to classify a stable length vector of input features. In this paper, we introduce an efficient algorithm for online signature length normalization by using Up-Sampling and Down-Sampling techniques. Furthermore, online signature verification system is also proposed by using both Principal Component Analysis (PCA) for feature extraction and Artificial Neural Network (ANN) for classification. The SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals, is used to evaluate the effectiveness of the proposed technique. Based on the tests performed, the proposed technique managed to achieve False Accept Rate (FAR) of 5.5% and False Reject Rate (FRR) of 8.75%. The Society of Digital Information and Wireless Communications 2015-01 Article PeerReviewed Malallah, Fahad Layth and Syed Ahmad, Sharifah Mumtazah and Wan Adnan, Wan Azizun and Arigbabu, Olasimbo Ayodeji and Iranmanesh, Vahab and Yussof, Salman (2015) Online handwritten signature recognition by length normalization using up-sampling and down-sampling. International Journal of Cyber-Security and Digital Forensics, 4 (1). pp. 302-313. ISSN 2305-0012 http://sdiwc.net/digital-library/online-handwritten-signature-recognition-by-length-normalization-using-upsampling-and-downsampling |
| spellingShingle | Malallah, Fahad Layth Syed Ahmad, Sharifah Mumtazah Wan Adnan, Wan Azizun Arigbabu, Olasimbo Ayodeji Iranmanesh, Vahab Yussof, Salman Online handwritten signature recognition by length normalization using up-sampling and down-sampling |
| title | Online handwritten signature recognition by length normalization using up-sampling and down-sampling |
| title_full | Online handwritten signature recognition by length normalization using up-sampling and down-sampling |
| title_fullStr | Online handwritten signature recognition by length normalization using up-sampling and down-sampling |
| title_full_unstemmed | Online handwritten signature recognition by length normalization using up-sampling and down-sampling |
| title_short | Online handwritten signature recognition by length normalization using up-sampling and down-sampling |
| title_sort | online handwritten signature recognition by length normalization using up sampling and down sampling |
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