Facial Expression Recognition in Real Time Using Convolutional Neural Network

Dhankar, Vashi; Rathee, Anu

doi:10.1002/9781119752134.ch5

Search citation statements

Order By: Relevance

Paper Sections

Select...

Ceca-net: Convolutional Neural Network Based On Eca-net1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNNs are often used for facial expression recognition 17 – 23 Our CNN model is shown below (called BASE, Fig. 2).…”

Section: Ceca-net: Convolutional Neural Network Based On Eca-netmentioning

confidence: 99%

Facial expression recognition algorithm based on efficient channel attention

et al. 2022

View full text Add to dashboard Cite

Facial expression recognition plays an increasingly important role in many fields.The richness of facial expressions and the ambiguity of expression recognition increase the difficulty of facial expression recognition. The recognition rate of existing algorithms is either high or complex. Either the complexity is low or the recognition rate is also low. Can a compromise be made between complexity and recognition rate? So, we designed a basic and effective convolutional neural networks (BASE) algorithm. In order to further improve the performance of the BASE algorithm, we propose a facial expression recognition algorithm based on efficient channel attention for deep convolutional neural networks (CECA-NET). We tested it on four commonly used facial expression databases. The efficient channel attention module increases the interactivity between channels and the key information that is not easy to be paid attention to is excavated. Experiments show that our method can obtain richer facial expression information and has stronger generalization ability.

show abstract

“…CNNs are often used for facial expression recognition 17 – 23 Our CNN model is shown below (called BASE, Fig. 2).…”

Section: Ceca-net: Convolutional Neural Network Based On Eca-netmentioning

confidence: 99%