Micro-expressions (MEs) are brief, subtle facial expressions that reveal concealed emotions, offering key behavioral cues for social interaction. Characterized by short duration, low intensity, and spontaneity, MEs have been mostly studied through subjective coding, lacking objective, quantitative indicators. This paper explores ME characteristics using facial electromyography (EMG), analyzing data from 147 macro-expressions (MaEs) and 233 MEs collected from 35 participants. First, regarding external characteristics, we demonstrate that MEs are short in duration and low in intensity. Precisely, we proposed an EMG-based indicator, the percentage of maximum voluntary contraction (MVC%), to measure ME intensity. Moreover, we provided precise interval estimations of ME intensity and duration, with MVC% ranging from 7% to 9.2% and the duration ranging from 307 ms to 327 ms. This research facilitates fine-grained ME quantification. Second, regarding the internal characteristics, we confirm that MEs are less controllable and consciously recognized compared to MaEs, as shown by participants' responses and self-reports. This study provides a theoretical basis for research on ME mechanisms and real-life applications. Third, building on our previous work, we present CASMEMG, the first public ME database including EMG signals, providing a robust foundation for studying micro-expression mechanisms and movement dynamics through physiological signals.

Micro-expressions (MEs) are brief, involuntary facial movements critical for detecting lies, drawing growing interest in psychology and computer science. However, annotating ME can burden human coders with excessive time commitment and overwhelming information that compromises coding reliability and efficiency. Such difficulties in data annotation also led to the small sample size problem and hindered the development of ME analysis. Specifically, our psychological research highlights the complexities involved in human annotation of key frames. To facilitate the annotating process of ME, we proposed the Micro-Expression Key Frame Inference (ME-KFI) problem, aiming to identify MEs’ temporal locations from a single frame, reducing manual annotation effort. We propose a Micro-Expression Contrastive Identification Annotation (MECIA) method as a solution to ME-KFI, including three modules: a contrastive module, an identification module, and an annotation module, corresponding to the three steps of manual annotation. The network’s outputs infer the key frame of ME clips. MECIA demonstrates superior performance over random baselines on SAMM and CAS(ME)$^{2}$databases and maintains comparable recognition accuracy with ground-truth clips.

Micro-expression (ME) is a significant non-verbal communication clue that reveals one person's genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME)3. The contribution of this article is summarized as follows: (1) CAS(ME)3offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME)3provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME)3elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME)3provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information.

Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. Unfortunately, the small sample problem severely limits the automation of ME analysis. Furthermore, due to the brief and subtle nature of ME, ME spotting is a challenging task, and the performance is still not satisfactory yet. This challenge focuses on two tasks, i.e., the micro- and macro-expression spotting task, and the ME Generation task.

Micro-expression, which is generated by facial muscle movements, could be a crucial cue for deception detection. In the existed research investigating the relationship between facial muscles and deception detection, researchers have focused almost exclusively on two muscles, i.e., zygomaticus and corrugator supercilii, based on the theoretical basis that they are highly associated with positive and negative expressions. However, the aim of this study is to demonstrate the direct relationship between facial muscle movements and deception detection. Addressing this issue, this paper proposes an experimental paradigm with high ecological validity that uses electromyography (EMG) signals to precisely examine the role of facial muscle movements in deception detection. Moreover, we propose a vector-based sequential forward selection (VSFS) algorithm to identify the muscle (or muscle combination) most closely associated with lying. Based on our proposed approach, the importance of seven selected facial muscles was explored by comparing the corresponding facial EMG (fEMG) between truth and lying conditions. First, the present study found that the zygomaticus and corrugator supercilii could play important roles in deception detection, and our findings are consistent with existed research. Second, the experiment result verified that the muscles related to deception detection were consistent with those with higher frequency occurring in micro-expression. Moreover, the present study provides a theoretical basis that intelligent micro-expressions analysis could improve the lie detection performance by focusing on the area of the forehead, eyebrows, and cheeks.