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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Masala, G.L. Grosso, E. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Political Sci., Commun., Eng. & Inf. Technol., Comput. Vision Lab., Sassari, Italy (Masala, G.L.; Grosso, E.) |
| Abstract | The paper proposes an original method, derived from basic face recognition and classification research, which is a good candidate for an effective automotive application. The proposed approach exploits a single b/w camera, positioned in front of the driver, and a very efficient classification strategy, based on neural network classifiers. A peculiar feature of the work is the adoption of iconic data reduction, avoiding specific and time-consuming feature-based approaches. Though at an initial development stage, the method proved to be fast and robust compared to state of the art techniques; experimental results show real-time response and mean weighted accuracy near to 93%. The method requires a simple training procedure which can be certainly improved for real applications; moreover it can be easily integrated with techniques for automatic face-recognition of the driver. |
| Sponsorship | IEEE Intell. Transp. Syst. Soc. |
| Starting Page | 913 |
| Ending Page | 918 |
| File Size | 531896 |
| Page Count | 6 |
| File Format | |
| ISSN | 19310587 |
| e-ISBN | 9781467327558 |
| DOI | 10.1109/IVS.2013.6629583 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-23 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vehicles Training Fatigue Face Neurons Cameras Feature extraction |
| Content Type | Text |
| Resource Type | Article |
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