BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATION
The main aim of this work is to use a keystroke biometric system as a behavioral type of biometrics and to improve the accuracy and dependability of the system. In this proposed we've pre-processed the data of dynamic keystrokes by converting the feature to a one-dimensional vector. In featu...
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| Format: | Article |
| Language: | Arabic |
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Mustansiriyah University/College of Engineering
2016-09-01
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| Series: | Journal of Engineering and Sustainable Development |
| Subjects: | |
| Online Access: | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/722 |
| _version_ | 1797984845394083840 |
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| author | Zeina Waleed Abaas |
| author_facet | Zeina Waleed Abaas |
| author_sort | Zeina Waleed Abaas |
| collection | DOAJ |
| description |
The main aim of this work is to use a keystroke biometric system as a behavioral type of biometrics and to improve the accuracy and dependability of the system. In this proposed we've pre-processed the data of dynamic keystrokes by converting the feature to a one-dimensional vector. In feature extraction, we've used Wavelet Energy (WE) by implementing 2D dimensional Discrete Wavelet (2D-DWT) into four-level and computing the energy for the Singular Value Decomposition (SVD). SVD is computed on the result of the wavelet and saved in a file for training information. Wavelet transforms Daubchies “DBI” basic function has the advantage that provides a good energy localization in the frequency domain as other wavelet transforms and then using Elman networks (Backpropagation) for training and testing the system and it is useful in such areas as signal processing and prediction where time plays a dominant role.
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| first_indexed | 2024-04-11T07:08:12Z |
| format | Article |
| id | doaj.art-b59efe39e7f548cf94c38152553c142f |
| institution | Directory Open Access Journal |
| issn | 2520-0917 2520-0925 |
| language | Arabic |
| last_indexed | 2024-04-11T07:08:12Z |
| publishDate | 2016-09-01 |
| publisher | Mustansiriyah University/College of Engineering |
| record_format | Article |
| series | Journal of Engineering and Sustainable Development |
| spelling | doaj.art-b59efe39e7f548cf94c38152553c142f2022-12-22T04:38:17ZaraMustansiriyah University/College of EngineeringJournal of Engineering and Sustainable Development2520-09172520-09252016-09-01205BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATIONZeina Waleed Abaas0Building & Construction Department, University of Technology, Baghdad, Iraq. The main aim of this work is to use a keystroke biometric system as a behavioral type of biometrics and to improve the accuracy and dependability of the system. In this proposed we've pre-processed the data of dynamic keystrokes by converting the feature to a one-dimensional vector. In feature extraction, we've used Wavelet Energy (WE) by implementing 2D dimensional Discrete Wavelet (2D-DWT) into four-level and computing the energy for the Singular Value Decomposition (SVD). SVD is computed on the result of the wavelet and saved in a file for training information. Wavelet transforms Daubchies “DBI” basic function has the advantage that provides a good energy localization in the frequency domain as other wavelet transforms and then using Elman networks (Backpropagation) for training and testing the system and it is useful in such areas as signal processing and prediction where time plays a dominant role. https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/722BiometricsKeystrokeSingular Value DecompositionWavelet DBINeural Network |
| spellingShingle | Zeina Waleed Abaas BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATION Journal of Engineering and Sustainable Development Biometrics Keystroke Singular Value Decomposition Wavelet DBI Neural Network |
| title | BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATION |
| title_full | BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATION |
| title_fullStr | BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATION |
| title_full_unstemmed | BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATION |
| title_short | BIOMETRIC KEYSTROKE RECOGNITION BASED ON HYBRID SVD AND WAVELET FOR FEATURE TRANSFORMATION |
| title_sort | biometric keystroke recognition based on hybrid svd and wavelet for feature transformation |
| topic | Biometrics Keystroke Singular Value Decomposition Wavelet DBI Neural Network |
| url | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/722 |
| work_keys_str_mv | AT zeinawaleedabaas biometrickeystrokerecognitionbasedonhybridsvdandwaveletforfeaturetransformation |