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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gonzalez-Ortega, D. Diaz-Pernas, F.J. Anton-Rodriguez, M. Martinez-Zarzuela, M. Diez-Higuera, J.F. Boto-Giralda, D. |
| Copyright Year | 2009 |
| Abstract | In this paper, a vision-based system to detect the eyelid closure for driver alertness monitoring is presented. Similarity measures with three eye templates (open, nearly close, and close) were calculated from many different features, such as 1-D and 2-D histograms and horizontal and vertical projections, of a big set of rectangular eyes images. Two classifiers, Multi-Layer Perceptron and Support Vector Machine, were intensively studied to select the best with the sequential forward feature selection. The system is based on the selected Multi-Layer Perceptron classifier, which is used to measure PERCLOS (percentage of time eyelids are close). The monitoring system is implemented with a consumer-grade computer and a webcam with passive illumination, runs at 55 fps, and achieved an overall accuracy of 95.75% with videos with different users, environments and illumination. The system can be used to monitor driver alertness robustly in real time. |
| Starting Page | 1301 |
| Ending Page | 1306 |
| File Size | 1647353 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424447350 |
| DOI | 10.1109/ISDA.2009.226 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-30 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer vision Eyes Computerized monitoring multi-layer perceptron eyelid closure detection Time measurement driver alertness monitoring Support vector machines Histograms support vector machine Support vector machine classification Lighting Eyelids Multilayer perceptrons sequential forward selection |
| Content Type | Text |
| Resource Type | Article |
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