Facial palsy detection using pre-trained deep learning models: A comparative study

Amsalam, Ali Saber; Al-Naji, Ali; Daeef, Ammar Yahya

doi:10.1063/5.0209928

AIP Conference Proceedings

2024

DOI: 10.1063/5.0209928

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Facial palsy detection using pre-trained deep learning models: A comparative study

Ali Saber Amsalam,

Ali Al-Naji,

Ammar Yahya Daeef

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2024

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A Deep Learning Approach for Early Detection of Facial Palsy in Video Using Convolutional Neural Networks: A Computational Study

Arora,

Zaeem,

Garg

et al. 2024

Computers

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Facial palsy causes the face to droop due to sudden weakness in the muscles on one side of the face. Computer-added assistance systems for the automatic recognition of palsy faces present a promising solution to recognizing the paralysis of faces at an early stage. A few research studies have already been performed to handle this research issue using an automatic deep feature extraction by deep learning approach and handcrafted machine learning approach. This empirical research work designed a multi-model facial palsy framework which is a combination of two convolutional models—a multi-task cascaded convolutional network (MTCNN) for face and landmark detection and a hyperparameter tuned and parametric setting convolution neural network model for facial palsy classification. Using the proposed multi-model facial palsy framework, we presented results on a dataset of YouTube videos featuring patients with palsy. The results indicate that the proposed framework can detect facial palsy efficiently. Furthermore, the achieved accuracy, precision, recall, and F1-score values of the proposed framework for facial palsy detection are 97%, 94%, 90%, and 97%, respectively, for the training dataset. For the validation dataset, the accuracy achieved is 95%, precision is 90%, recall is 75.6%, and F-score is 76%. As a result, this framework can easily be used for facial palsy detection.

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A Deep Learning Approach for Early Detection of Facial Palsy in Video Using Convolutional Neural Networks: A Computational Study

Arora,

Zaeem,

Garg

et al. 2024

Computers

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show abstract

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Facial palsy detection using pre-trained deep learning models: A comparative study

Cited by 1 publication

References 18 publications

A Deep Learning Approach for Early Detection of Facial Palsy in Video Using Convolutional Neural Networks: A Computational Study

A Deep Learning Approach for Early Detection of Facial Palsy in Video Using Convolutional Neural Networks: A Computational Study

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