Micro-expressions (ME) are rapidly occurring expressions that reveal the true emotions that a human being is trying to hide, cover, or suppress. These expressions, which reveal a person\'s actual feelings, have a broad spectrum of applications in public safety and clinical diagnosis. This study provides a comprehensive review of the area of ME recognition. A bibliometric and network analysis techniques is used to compile all the available literature related to ME recognition. A total of 735 publications from the Web of Science (WOS) and Scopus databases were evaluated from December 2012 to December 2022 using all relevant keywords. The first round of data screening produced some basic information, which was further extracted for citation, coupling, co-authorship, co-occurrence, bibliographic, and co-citation analysis. Additionally, a thematic and descriptive analysis was executed to investigate the content of prior research findings, and research techniques used in the literature. The year wise publications indicated that the published literature between 2012 and 2017 was relatively low but however by 2021, a nearly 24-fold increment made it to 154 publications. The three topmost productive journals and conferences included IEEE Transactions on Affective Computing (n = 20 publications) followed by Neurocomputing (n = 17) and Multimedia tools and applications (n = 15). Zhao G was the most proficient author with 48 publications and the top influential country was China (620 publications). Publications by citations showed that each of the authors acquired citations ranging from 100 to 1225. While publications by organizations indicated that the University of Oulu had the most published papers (n = 51). Deep learning, facial expression recognition, and emotion recognition were among the most frequently used terms. It has been discovered that ME research was primarily classified in the discipline of engineering, with more contribution from China and Malaysia comparatively.

Research has demonstrated a relationship between anger and suicidality, while real-time authentic emotions behind facial expressions could be detected during advising hypothetical protagonists in life dilemmas. This study aimed to investigate the predictive validity of anger expressions during advising for suicide risk. Besides advising on life dilemmas (a friend\'s betrayal, a friend\'s suicide attempt), 130 adults completed the suicidal scale of the Mini-International Neuropsychiatric Interview. Participants\' anger during advice-giving was measured 29 times/s by artificial intelligence (AI)-based software FaceReader 7.1. The results showed that anger was a significant predictor of suicide risk. Increased anger during advising was associated with higher suicide risk. In contrast, there was no significant correlation between suicide risk and duration or length of advising. Therefore, measuring micro expressions of anger with AI-based software may help detect suicide risk among clinical patients in both traditional and online counseling contexts and help prevent suicide.