ArticleMultimedia tools and applications2019
Facial expression recognition for monitoring neurological disorders based on convolutional neural network.
Article in Multimedia tools and applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.
- Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis.Biomedical engineering online · 2025Pooled it
- Facial Expression Metrics as Digital Biomarkers of Neurologic Disease.Neurology open access · 2025Article
- Cross-modal gated feature enhancement for multimodal emotion recognition in conversations.Scientific reports · 2025Article
- Data Mining-Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach.JMIR formative research · 2025Article
- Predicting location emotions of users considering multidimensional spatio-temporal dependencies.Frontiers in psychology · 2025Article
- Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.Healthcare technology letters · 2024Article
- Towards Facial Gesture Recognition in Photographs of Patients with Facial Palsy.Healthcare (Basel, Switzerland) · 2022Article
- Article
- Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.Computational intelligence and neuroscience · 2022Article
- Methods for Facial Expression Recognition with Applications in Challenging Situations.Computational intelligence and neuroscience · 2022Article
- Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Facial expressions are a significant part of non-verbal communication. Recognizing facial expressions of people with neurological disorders is essential because these people may have lost a significant amount of their verbal communication ability. Such an assessment requires time consuming examination involving medical personnel, which can be quite challenging and expensive. Automated facial expression recognition systems that are low-cost and noninvasive can help experts detect neurological disorders. In this study, an automated facial expression recognition system is developed using a novel deep learning approach. The architecture consists of four-stage networks. The first, second and third networks segment the facial components which are essential for facial expression recognition. Owing to the three networks, an iconize facial image is obtained. The fourth network classifies facial expressions using raw facial images and iconize facial images. This four-stage method combines holistic facial information with local part-based features to achieve more robust facial expression recognition. Preliminary experimental results achieved 94.44% accuracy for facial expression recognition on RaFD database. The proposed system produced 5% improvement than the facial expression recognition system by using raw images. This study presents a quantitative, objective and non-invasive facial expression recognition system to help in the monitoring and diagnosis of neurological disorders influencing facial expressions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.