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http://hdl.handle.net/2080/3774
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DC Field | Value | Language |
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dc.contributor.author | Sahoo, Goutam Kumar | - |
dc.contributor.author | Ponduru, Jayakrishna | - |
dc.contributor.author | Das, Santos Kumar | - |
dc.contributor.author | Singh, Poonam | - |
dc.date.accessioned | 2022-12-01T12:47:12Z | - |
dc.date.available | 2022-12-01T12:47:12Z | - |
dc.date.issued | 2022-11 | - |
dc.identifier.citation | IEEE 19th India Council International Conference (INDICON), Kochi, Kerala, 24th - 26th November 2022 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/3774 | - |
dc.description | Copyright belongs to proceeding publisher | en_US |
dc.description.abstract | This work presents a deep learning-based approach for the evaluation of facial expression recognition (FER) performance. The main objective is to develop a deep convolutional neural network (CNN) to perform FER using the publicly available benchmark dataset, the FER2013 dataset. The FER2013 dataset includes hand-based facial occlusion, incorrectly cropped or partial images, images with glasses, low-resolution images, etc., which are close to real driving complex scenarios. Two custom CNN models and a pre-trained VGG16 model are evaluated for the FER task. The deep CNN model with 10-layer architecture shows the best performance accuracy of 68.34%. This deep CNN model can be used to monitor driver behavior from front face images captured via dashboard camera and alert the driver to improve their driving style for a safe drive. | en_US |
dc.subject | Facial Expression Recognition (FER) | en_US |
dc.subject | Driver Behavior | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | FER2013 Dataset | en_US |
dc.subject | Driving Safety | en_US |
dc.title | Deep Leaning-Based Facial Expression Recognition in FER2013 Database: An in-Vehicle Application | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2022_INDICON_SKDas_Deep.pdf | 711.1 kB | Adobe PDF | View/Open |
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