Emotion recognition and interaction of smart education environment screen based on deep learning networks

Abstract: Smart education environments combine technologies such as big data, cloud computing, and artificial intelligence to optimize and personalize the teaching and learning process, thereby improving the efficiency and quality of education. This article proposes a dual-stream-coded image sentiment analysis method based on both facial expressions and background actions to monitor and analyze learners’ behaviors in real time. By integrating human facial expressions and scene backgrounds, the method can effectively address the occlusion problem in uncontrolled environments. To enhance the accuracy and efficiency of emotion recognition, a multi-task convolutional network is employed for face extraction, while 3D convolutional neural networks optimize the extraction process of facial features. Additionally, the adaptive learning screen adjustment system proposed in this article dynamically adjusts the presentation of learning content to optimize the learning environment and enhance learning efficiency by monitoring learners’ expressions and reactions in real time. By analyzing the experimental results on the Emotic dataset, the emotion recognition model in this article shows high accuracy, especially in the recognition of specific emotion categories. This research significantly contributes to the field of smart education environments by providing an effective solution for real-time emotion recognition.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Emotion recognition and interaction of smart education environment screen based on deep learning networks ; volume:34 ; number:1 ; year:2025 ; extent:14 ; Journal of intelligent systems ; 34, Heft 1 (2025) (gesamt 14)

Contributors, Places and Time

• Creator: Zhao, Wei ; Qiu, Liguo

Further information

• DOI: 10.1515/jisys-2024-0082
• URN: urn:nbn:de:101:1-2503110605301.804474325913
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:33 AM CEST