Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/3774
Title: | Deep Leaning-Based Facial Expression Recognition in FER2013 Database: An in-Vehicle Application |
Authors: | Sahoo, Goutam Kumar Ponduru, Jayakrishna Das, Santos Kumar Singh, Poonam |
Keywords: | Facial Expression Recognition (FER) Driver Behavior Deep Learning FER2013 Dataset Driving Safety |
Issue Date: | Nov-2022 |
Citation: | IEEE 19th India Council International Conference (INDICON), Kochi, Kerala, 24th - 26th November 2022 |
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. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/3774 |
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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