ICTACT Journal on Image and Video Processing

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Facial Emotion Recognition Using Convolutional Neural Network


Affiliations
1 Department of Computer Engineering, Tribhuvan University, Nepal
     

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Facial expressions play a significant role in social communication since they convey a lot of information about people, such as moods, emotions, and other things. Many researchers gained an optimal accuracy in most of the popular facial recognition datasets: CK+, JAFFE, IEV, but in FER2013 the best model accuracy is about 74%. This article purpose deep learning-based models to mitigate this issue. Three models based on AlexNet, VGG19, and ResNet50 are used to train with the dataset, and the very best model among them is further analyzed. The best model is trained using various optimizers and evaluated based on its training and testing accuracy, confusion matrix, ROC Curve. The finest model gained an accuracy of 91.89504% which is better than past state of art models by at least 17% accuracy.

Keywords

Facial Expression, Confusion Matrix, Emotion, Optimizer, Receiver Operating Characteristic Curve.
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  • Facial Emotion Recognition Using Convolutional Neural Network

Abstract Views: 359  |  PDF Views: 1

Authors

Milan Tripathi
Department of Computer Engineering, Tribhuvan University, Nepal

Abstract


Facial expressions play a significant role in social communication since they convey a lot of information about people, such as moods, emotions, and other things. Many researchers gained an optimal accuracy in most of the popular facial recognition datasets: CK+, JAFFE, IEV, but in FER2013 the best model accuracy is about 74%. This article purpose deep learning-based models to mitigate this issue. Three models based on AlexNet, VGG19, and ResNet50 are used to train with the dataset, and the very best model among them is further analyzed. The best model is trained using various optimizers and evaluated based on its training and testing accuracy, confusion matrix, ROC Curve. The finest model gained an accuracy of 91.89504% which is better than past state of art models by at least 17% accuracy.

Keywords


Facial Expression, Confusion Matrix, Emotion, Optimizer, Receiver Operating Characteristic Curve.

References