Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3774
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dc.contributor.authorSahoo, Goutam Kumar-
dc.contributor.authorPonduru, Jayakrishna-
dc.contributor.authorDas, Santos Kumar-
dc.contributor.authorSingh, Poonam-
dc.date.accessioned2022-12-01T12:47:12Z-
dc.date.available2022-12-01T12:47:12Z-
dc.date.issued2022-11-
dc.identifier.citationIEEE 19th India Council International Conference (INDICON), Kochi, Kerala, 24th - 26th November 2022en_US
dc.identifier.urihttp://hdl.handle.net/2080/3774-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThis 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.subjectFacial Expression Recognition (FER)en_US
dc.subjectDriver Behavioren_US
dc.subjectDeep Learningen_US
dc.subjectFER2013 Dataseten_US
dc.subjectDriving Safetyen_US
dc.titleDeep Leaning-Based Facial Expression Recognition in FER2013 Database: An in-Vehicle Applicationen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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