Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model

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.

Yaghouti Fatemeh; MOTAMED SARA

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

Journal Paper

Paper Information

Journal: Advances in Cognitive Sciences Year:2019 | Volume:20 | Issue:4 Page(s): 46-61

Download Full-Text

Persian Verion

View:

1,073

Download:

Cites:

Information Journal Paper

Title

Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model

Author(s)

Yaghouti Fatemeh | MOTAMED SARA | Issue Writer Certificate

Keywords

Facial Expression of EmotionQ1

feature extractionQ1

Brain Emotional LearningQ1

Communication matrixQ1

Abstract

Introduction: Facial expressions are one of the most important ways of communication and human response to the surrounding environment. The purpose of this study is to use the brain’ s emotional learning model (BEL) to face emotion recognition. The brain’ s emotional learning model is inspired by the human brain’ s limbic system, which is responsible for motivating human emotions. This model has been used to improve the recognition rate of emotional expression of the human face Method: The input of the proposed model is JAFFE standard dataset which includes six emotional expressions of Happiness, Sadness, Anger, Surprise, Fear and Disgust. After reading images using the MATLAB software commands, all read images will be entered into the extracting step. Also, The PCA method was used to extract the small image components. Finally, to calculate the recognition rate of facial expressions, all extracted features from the previous step are entered into the classification stage of the BEL model. In the application of the BEL method, the Communication matrix with the components of the eyebrows, eyes and mouth is created and their dependence is determined in each emotion. This way you can recognize facial expressions. Also, because of the demonstration of the efficiency of the proposed model, the BEL model is compared with the SVM rival model. Results: Dataset analysis results show the recognition rate of facial expressions of 93. 8% Conclusion: According to the results of this research, the BEL model shows the rate of recognition of emotional expressions with higher accuracy than the SVM model.

Cites

No record.

References

No record.

Cite

APA: Copy

Yaghouti, Fatemeh, & MOTAMED, SARA. (2019). Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model. ADVANCES IN COGNITIVE SCIENCE, 20(4 ), 46-61. SID. https://sid.ir/paper/82902/en

Vancouver: Copy

Yaghouti Fatemeh, MOTAMED SARA. Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model. ADVANCES IN COGNITIVE SCIENCE[Internet]. 2019;20(4 ):46-61. Available from: https://sid.ir/paper/82902/en

IEEE: Copy

Fatemeh Yaghouti, and SARA MOTAMED, “Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model,” ADVANCES IN COGNITIVE SCIENCE, vol. 20, no. 4 , pp. 46–61, 2019, [Online]. Available: https://sid.ir/paper/82902/en

Related Journal Papers