Recognizing Microexpression: An Interdisciplinary Perspective

As a Chinese saying goes, “Look at the weather when you step out; look at men’s faces when you step in.” Recognizing expressions is a very common activity in daily life. People can infer someone’s inner emotions from his or her facial expressions. However, not everyone writes their emotion on their face; someone may suppress true emotion and express a false facial expression depending on politeness, context, culture, or status. The suppressed expressions can be expressed fleetingly in the form of microexpressions, which usually last only 1/25 to 1/5 second. Microexpressions were of importance for many practical applications because it reflects the true inner feeling, such as national security, deception detection, clinical therapy, emotion analysis, and human-computer interaction. The recognition of microexpressions is the premise of application of microexpression and now the recognition of microexpressions are getting more and more attention. However, perceiving other’s microexpressions is not easy. The context, culture, and perceiver himself affect the recognition of microexpression. There are considerable efforts in the field of psychology, neuroscience, and computer science to recognize facial microexpressions. This Researc Topic illuminates the latest advances in interdisciplinary understanding how microexpressions are perceived and recognized. The authors contribute from diverse perspectives in the current research topic by using behavioral experiment, EEG, fMRI, and computer vision techniques. They investigated how humans recognize macroexpressions and microexpressions in term of modulating factors (e.g., gender, duration) and the underlying neural mechanisms, and how machine recognition algorithms and models are developed and inspired by the human recognition data. The Research Topic reveals that research on the recognition of microexpressions is diverse but progressing. This is not surprising given that this topic receives more and more attention due to its promising potential applications. As new techniques and theories develop, it is likely that efficient and effective algorithms for recognizing microexpression will become possible. We hope that these articles provide a look into that future.