Quantitative comparison of motion history image variants for video-based depression assessment

Author:

Pampouchidou AnastasiaORCID,Pediaditis Matthew,Maridaki Anna,Awais Muhammad,Vazakopoulou Calliope-Marina,Sfakianakis Stelios,Tsiknakis Manolis,Simos Panagiotis,Marias Kostas,Yang Fan,Meriaudeau Fabrice

Funder

State Scholarships Foundation

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Information Systems,Signal Processing

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion;Proceedings of the IEEE;2023-10

2. Kullback-Leibler Divergence Based Method for Depression Diagnosis Using Video Data;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06

3. Effectiveness of Mental Health Care by Using Machine Learning on Manufacturing Worker;International Journal of Precision Engineering and Manufacturing-Smart Technology;2023-07-01

4. Deep Emotions Recognition from Facial Expressions using Deep Learning;VFAST Transactions on Software Engineering;2023-06-19

5. Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks;Electronics;2023-01-08

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