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Enhancement of offline signature verification using support vector machine / John Ullyses O. Velaroso, Sherwin Carlo R. Cruz. 6

By: John Ullyses O. Velaroso, Sherwin Carlo R. Cruz. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; November 2022.46Edition: Description: 28 cm. 122 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

Contents:

Action note: In: Summary: ABSTRACT: Signatures are commonly used for personal verification or signifying an agreement. Therefore, confirming whether a signature is genuine has become a priority in information security. Signature verification methods are continually developed to automate and hasten the process. A study utilized Support Vector Machine to verify offline signatures. This paper proposed an enhancement of the said algorithm by adding preprocessing techniques, using a more robust set of feature extraction methods, utilizing a writer-dependent classifier, and computing the best hyperparameter values of C and Gamma using the Giza Pyramid Construction Algorithm. Two datasets of offline signatures were used to train and test the algorithms. Results showed that the enhanced algorithm significantly performed better than the existing algorithm, with an accuracy score of 96% and FAR and FRR both having 4% using the CEDAR dataset. The GPDS-300 resulted to an accuracy score of 92%, FAR of 11% and FRR of 6%. In comparison to the initial algorithm that resulted with an accuracy score of 83% and, FRR both having 17% using the CEDAR dataset, and 83% accuracy score as well in the GPDS-300, FAR of 17%, and FRR of 16%. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	T QA76.9.U55.2022 (Browse shelf)	Available		FT7753

Total holds: 0

Undergraduate Thesis: (Bachelor of Science in Computer Science) Pamantasan ng Lungsod ng Maynila, 2022. 56

ABSTRACT: Signatures are commonly used for personal verification or signifying an agreement. Therefore, confirming whether a signature is genuine has become a priority in information security. Signature verification methods are continually developed to automate and hasten the process. A study utilized Support Vector Machine to verify offline signatures. This paper proposed an enhancement of the said algorithm by adding preprocessing techniques, using a more robust set of feature extraction methods, utilizing a writer-dependent classifier, and computing the best hyperparameter values of C and Gamma using the Giza Pyramid Construction Algorithm. Two datasets of offline signatures were used to train and test the algorithms. Results showed that the enhanced algorithm significantly performed better than the existing algorithm, with an accuracy score of 96% and FAR and FRR both having 4% using the CEDAR dataset. The GPDS-300 resulted to an accuracy score of 92%, FAR of 11% and FRR of 6%. In comparison to the initial algorithm that resulted with an accuracy score of 83% and, FRR both having 17% using the CEDAR dataset, and 83% accuracy score as well in the GPDS-300, FAR of 17%, and FRR of 16%.

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