Scalable Real-time Emotion Recognition using EfficientNetV2 and Resolution Scaling

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ghadami Omid; Rezvanian Alireza; Shakuri Saeed

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

Download Full-Text

View:

Download:

Information Seminar Paper

Title

Scalable Real-time Emotion Recognition using EfficientNetV2 and Resolution Scaling

Author(s)

Ghadami Omid | Rezvanian Alireza | Shakuri Saeed

Keywords

Real-Time Facial Emotion Recognition

Deep Learning

CNN

Efficientnetv2

Imbalanced Datasets

Resolution Scaling

Abstract

Facial emotion recognition has been extensively researched in recent years due to many use cases. The most important applications are increasing human-computer interaction and helping with autism spectrum disorders. Also, in most applications, real-time execution is required. The model and computational resource are two main factors of the inference time. Hence, to propose a real-time method, it is required to concentrate on these two factors. In this paper, we utilized Efficientnetv2 due to its efficiency. Furthermore, we proposed a scalable method based on Resolution Scaling to keep the model in real-time in different computational resources and models. This scalable method has been implemented using a polynomial equation to find the best value of the resolution for a specific inference time based on our hardware and model. Thus, the main objective of this paper is to propose a scalable real-time method for the facial emotion recognition task using Resolution Scaling. Consequently, using a polynomial equation for Resolution Scaling, we proposed Scalable-ENV2B0 and Scalable-ENV2S based on Efficientnetv2B0 and Efficientnetv2S, respectively. According to the ultimate results on the KDEF dataset, Scalable-ENV2B0 can classify (302, 302, 3) input size images in real time on our hardware. Also, this model achieved an impressive 96% accuracy on KDEF, which outperforms previous real-time studies based on our knowledge. However, the main advantage of the proposed method is scalability, which hasn’, t been addressed in this task so far.

Video

Cites

No record.

References

No record.

Cite

APA: Copy

Ghadami, Omid, Rezvanian, Alireza, & Shakuri, Saeed. (2024). Scalable Real-time Emotion Recognition using EfficientNetV2 and Resolution Scaling. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147371/en

Vancouver: Copy

Ghadami Omid, Rezvanian Alireza, Shakuri Saeed. Scalable Real-time Emotion Recognition using EfficientNetV2 and Resolution Scaling. 2024. Available from: https://sid.ir/paper/1147371/en

IEEE: Copy

Omid Ghadami, Alireza Rezvanian, and Saeed Shakuri, “Scalable Real-time Emotion Recognition using EfficientNetV2 and Resolution Scaling,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147371/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops