Cargando…
Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition
Understanding a person’s feelings is a very important process for the affective computing. People express their emotions in various ways. Among them, facial expression is the most effective way to present human emotional status. We propose efficient deep joint spatiotemporal features for facial expr...
Autores principales: | Jeong, Dami, Kim, Byung-Gyu, Dong, Suh-Yeon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180996/ https://www.ncbi.nlm.nih.gov/pubmed/32235662 http://dx.doi.org/10.3390/s20071936 |
Ejemplares similares
-
Expression-Guided Deep Joint Learning for Facial Expression Recognition
por: Fang, Bei, et al.
Publicado: (2023) -
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
por: Minaee, Shervin, et al.
Publicado: (2021) -
EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-Time Facial Expression Recognition
por: Lee, James Ren, et al.
Publicado: (2021) -
Spatiotemporal neural network dynamics for the processing of dynamic facial expressions
por: Sato, Wataru, et al.
Publicado: (2015) -
The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition
por: Barros, Pablo, et al.
Publicado: (2020)