Improved Methods for Automatic Facial Expression Recognition

Improved Methods for Automatic Facial Expression Recognition

Facial Expression

Machine Learning

Deep Learning

Facial Land- Marks

Convolution Neural Network [CNN]

Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems.  In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK.  To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing.

Echoukairi, Hassan

El Ghmary, Mohamed

Ziani, Said

Ouacha, Ali

International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 06 (2023); pp. 33-44

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2023-03-21

Copyright (c) 2023 Mohamed El Ghmary, Said ZIANI, Hassan ECHOUKAIRI, Ali OUACHA

https://creativecommons.org/licenses/by/4.0

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eng

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info:eu-repo/semantics/publishedVersion

Peer-reviewed Article