Implementation of facial emotion recognition using CNN on jetson TX2
Corressponding author's email:
phuctq@hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.63.2021.38Keywords:
recognition, facial emotion, neural network, CNN, Jetson TX2Abstract
In this paper, a convolutional neural network (CNN), one of the most popular deep learning architectures used for facial extraction research, has been implemented on NVIDIA Jetson TX2 hardware. Different from many existing approaches investigating CNN with complex structure and large parameters, we have focused on building a robust neural network through extensive performance comparison and evaluation. In addition, we have collected a dataset using a built-in camera on a laptop computer. Specifically, we have applied our model on Jetson TX2 hardware to take advantage of the computational power of the embedded GPU to optimize computation time and data training. In particular, both FER2013 and RAF datasets with seven basic emotions have been used for training and testing purposes. Finally, the evaluation results show that the proposed method achieves an accuracy of up to 72% on the testing dataset.
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