Advanced Technologies and Applications of Emotion Recognition

Dear Colleagues,

Emotions are physical and mental states brought on by neurophysiological changes, encompassing a range of feelings, thoughts and behaviors. Human emotions provide crucial information in both psychology and physiology. Emotion recognition, the process of identifying emotion, can significantly benefit areas such as healthcare, human–computer interaction, and customer service. For instance, emotion information can support the diagnosis and monitoring of mental health conditions and provide feedback to therapists. Understanding the emotions of users better can enhance user experience by making human–computer interfaces more responsive and adaptive to emotional states. Additionally, analyzing consumer emotions can help researchers to tailor marketing strategies and improve customer satisfaction. Empowered by the novel algorithms in machine learning, particularly in deep learning, along with the availability of datasets, people have made impressive progress in emotion recognition studies.

However, challenges remain in the field of emotion recognition. Theoretically, various aspects of emotion recognition paradigms, such as dimensions and metrics, warrant further study. There are also model-related challenges, including issues due to generalization, subject-dependencies, and modality-dependencies. Other than that, there are plenty of potential applications of emotion recognition, such as some specific healthcare applications, that remain underexplored. Additionally, current publicly available datasets for emotion recognition fall short of supporting comprehensive research needs.

We are delighted to announce this Special Issue dedicated to the burgeoning field of emotion recognition, inviting researchers and practitioners to submit their cutting-edge work on advanced technologies and innovative applications. This Special Issue will provide a comprehensive platform for the latest developments, fostering collaboration and sharing insights that drive the future of emotion recognition.

Recommended topics include, but are not limited to, the following:

• Applications of emotion recognition (e.g., healthcare, human–computer interaction);
• Novel AI models and approaches for emotion recognition;
• Theory and paradigm of emotion recognition (e.g., dimension and metrics);
• Multimodal emotion recognition;
• Sensors and hardware for emotion recognition;
• Datasets for emotion recognition.

Dr. Xiaoming Zhang
Dr. Beiming Cao
Dr. Haoran Wei
Guest Editors

Manuscript Submission Information

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