ASD-EVNet: An Ensemble Vision Network based on Facial Expression for Autism Spectrum Disorder Recognition
Conference paper
Jaby, A., Islam, M. B. and Ahad, M. A. R. 2023. ASD-EVNet: An Ensemble Vision Network based on Facial Expression for Autism Spectrum Disorder Recognition. 18th International Conference on Machine Vision and Applications (MVA). Hamamatsu, Japan 23 - 25 Jul 2023 IEEE. https://doi.org/10.23919/MVA57639.2023.10215688
Authors | Jaby, A., Islam, M. B. and Ahad, M. A. R. |
---|---|
Type | Conference paper |
Abstract | Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects individuals’ social interaction, communication, and behavior. Early diagnosis and intervention are critical for the well-being and development of children with ASD. Available methods for diagnosing ASD are unpredictable (or with limited accuracy) or require significant time and resources. We aim to enhance the precision of ASD diagnosis by utilizing facial expressions, a readily accessible and limited time-consuming approach. This paper presents ASD Ensemble Vision Network (ASD-EVNet) for recognizing ASD based on facial expressions. The model utilizes three Vision Transformer (ViT) architectures, pre-trained on imageNet-21K and fine-tuned on the ASD dataset. We also develop an extensive collection of facial expression-based ASD dataset for children (FADC). The ensemble learning model was then created by combining the predictions of the three ViT models and feeding it to a classifier. Our experiments demonstrate that the proposed ensemble learning model outperforms and achieves state-of-the-art results in detecting ASD based on facial expressions. |
Year | 2023 |
Conference | 18th International Conference on Machine Vision and Applications (MVA) |
Publisher | IEEE |
Accepted author manuscript | License File Access Level Anyone |
Publication dates | |
Online | 22 Aug 2023 |
Publication process dates | |
Deposited | 28 Nov 2024 |
Book title | 2023 18th International Conference on Machine Vision and Applications (MVA) |
ISBN | 978-4-88552-343-4 |
Digital Object Identifier (DOI) | https://doi.org/10.23919/MVA57639.2023.10215688 |
Copyright holder | © 2023 IEEE |
Copyright information | Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
https://repository.uel.ac.uk/item/8y99z
Download files
11
total views4
total downloads10
views this month3
downloads this month