Deep Learning Based on Face Emotion Recognition using an Artificial Neural Network

Pushpavalli Mohan, Hsien Tsung Chang*

*此作品的通信作者

研究成果: 圖書/報告稿件的類型會議稿件同行評審

摘要

Facial Emotion Recognition has become a critical research domain in Artificial Intelligence due to its vital applications across multiple fields, including security and healthcare. This study aims to overcome prevailing limitations of current methodologies, which primarily rely on static image frames and neutral facial expressions, hindering optimal emotion recognition rates. Leveraging advanced deep learning techniques, this research focused on the identification of seven human emotions - happiness, anger, disgust, fear, sadness, surprise, and neutrality - using both static and dynamic images from CK+ and FER datasets. The research experimented with various models such as Lightweight MobileNet and Artificial Neural Networks, aiming for enhanced accuracy in real-time emotion recognition applications. A novel visualization technique was developed to pinpoint the crucial facial regions integral for detecting distinct emotions by analyzing classifier outputs, revealing that different emotions are sensitive to different facial areas. The results emphasize the value of specific facial regions in conveying and recognizing emotions and illustrate the substantial promise of deep learning methodologies in advancing Facial Emotion Recognition technology.

原文英語
主出版物標題Proceedings - 2023 International Conference on Artificial Intelligence and Power Engineering, AIPE 2023
發行者Institute of Electrical and Electronics Engineers Inc.
頁面19-23
頁數5
ISBN(電子)9798350310788
DOIs
出版狀態已出版 - 2023
事件2023 International Conference on Artificial Intelligence and Power Engineering, AIPE 2023 - Tokyo, 日本
持續時間: 20 10 202322 10 2023

出版系列

名字Proceedings - 2023 International Conference on Artificial Intelligence and Power Engineering, AIPE 2023

Conference

Conference2023 International Conference on Artificial Intelligence and Power Engineering, AIPE 2023
國家/地區日本
城市Tokyo
期間20/10/2322/10/23

文獻附註

Publisher Copyright:
© 2023 IEEE.

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