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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Weng Kin Lai Beng Ghee Tan Ming Siong Soo Khan, I. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., IIUM, Kuala Lumpur, Malaysia (Khan, I.) || Electr. & Electron. Eng. Dept., Tunku Abdul Rahman Univ. Coll., Kuala Lumpur, Malaysia (Weng Kin Lai) || Mech. Eng. Dept., Tunku Abdul Rahman Univ. Coll., Kuala Lumpur, Malaysia (Beng Ghee Tan; Ming Siong Soo) |
| Abstract | The application of the Cognitive RAM (CogRAM) weightless neural net in testing a keystroke biometrics user authentication system for a numeric keypad is discussed in this paper. The two-factor user authentication system developed here uses the common password that is complemented with the keystroke patterns of the users. The keystroke pattern is represented by the force applied to constitute a fixed length passkey to compose a complete pattern for the entered password. The system has been designed and developed around an 8-bit microcontroller, based on the AVR enhanced RISC architecture. The preliminary experimental results showed that the designed system can successfully authenticate the unique and consistent keystroke biometric patterns of the users. |
| Starting Page | 3751 |
| Ending Page | 3758 |
| File Size | 3943817 |
| Page Count | 8 |
| File Format | |
| ISSN | 21614407 |
| e-ISBN | 9781479914845 |
| DOI | 10.1109/IJCNN.2014.6889702 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-07-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Force Biometrics (access control) Authentication Keyboards Training Sensors Computer architecture |
| Content Type | Text |
| Resource Type | Article |
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