Research Topic

Facial Expression Recognition and Computing: An Interdisciplinary Perspective

About this Research Topic

Through the configuration of facial muscles, facial expressions are assumed to reflect a person’s internal feelings, emotions, motives and needs. It is one of the most heatedly discussed topics in psychology, cognitive neuroscience, and computer science. The general view of emotion recognition may be traced back to Darwin in 1872 when he proposed that human emotions and expressions were innate and universal. In 1992, Ekman proposed six basic emotions: anger, disgust, fear, happiness, sadness, and surprise that people from all cultures could easily read from facial expression.

According to the current knowledge, recognition of facial expression is carried out by a number of interconnected and distributed brain regions. Meanwhile, automatic recognition of facial expression using machine learning technique is also a very popular topic. Some computing methods of automatic recognition are based on the theory of Facial Action Coding system (FACS), which was proposed by Ekman back in 1976. While others do not rely on the theory too much and instead, they input the facial image as a whole and use advanced models such as Deep Neural Network to extract high-level features directly for the facial expression recognition.

The overall goal of this topic is to explore the latest developments in facial expression recognition,aiming to further understand the psychological and cognitive mechanism of how human processing expressions. The scope of this topic includes processing expressions for normal people as well as people with mental disorders, such as depression, schizophrenia, etc. We particularly welcome contributions discussing the computer-based recognition of facial expressions and emotions, the comparison of the similarities and differences between machine recognition and human recognition, and the trend of machine recognition of emotions. The sub-themes include, but are not limited to the following:

• Characteristics of normal people's facial expression recognition
• Recognition of characteristics of patients with depression and other psychiatric disorders
• Features of facial expression recognition using machine learning
• Comparison between machine recognition and human recognition
• Micro expression recognition
• Emotion recognition using multimodal signals


Keywords: Facial Expression, Affective Computing, Deep Learning, Recognition, Action Unit


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Through the configuration of facial muscles, facial expressions are assumed to reflect a person’s internal feelings, emotions, motives and needs. It is one of the most heatedly discussed topics in psychology, cognitive neuroscience, and computer science. The general view of emotion recognition may be traced back to Darwin in 1872 when he proposed that human emotions and expressions were innate and universal. In 1992, Ekman proposed six basic emotions: anger, disgust, fear, happiness, sadness, and surprise that people from all cultures could easily read from facial expression.

According to the current knowledge, recognition of facial expression is carried out by a number of interconnected and distributed brain regions. Meanwhile, automatic recognition of facial expression using machine learning technique is also a very popular topic. Some computing methods of automatic recognition are based on the theory of Facial Action Coding system (FACS), which was proposed by Ekman back in 1976. While others do not rely on the theory too much and instead, they input the facial image as a whole and use advanced models such as Deep Neural Network to extract high-level features directly for the facial expression recognition.

The overall goal of this topic is to explore the latest developments in facial expression recognition,aiming to further understand the psychological and cognitive mechanism of how human processing expressions. The scope of this topic includes processing expressions for normal people as well as people with mental disorders, such as depression, schizophrenia, etc. We particularly welcome contributions discussing the computer-based recognition of facial expressions and emotions, the comparison of the similarities and differences between machine recognition and human recognition, and the trend of machine recognition of emotions. The sub-themes include, but are not limited to the following:

• Characteristics of normal people's facial expression recognition
• Recognition of characteristics of patients with depression and other psychiatric disorders
• Features of facial expression recognition using machine learning
• Comparison between machine recognition and human recognition
• Micro expression recognition
• Emotion recognition using multimodal signals


Keywords: Facial Expression, Affective Computing, Deep Learning, Recognition, Action Unit


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

31 March 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

31 March 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..