The Effect of Facial Self-Resemblance on Emotional Mimicry

Social resemblance, like group membership or similar attitudes, increases the mimicry of the observed emotional facial display. In this study, we investigate whether facial self-resemblance (manipulated by computer morphing) modulates emotional mimicry in a similar manner. Participants watched dynamic expressions of faces that either did or did not resemble their own, while their facial muscle activity was measured using EMG. Additionally, after each presentation, respondents completed social evaluations of the faces they saw. The results show that self-resemblance evokes convergent facial reactions. More specifically, participants mimicked the happiness and, to a lesser extent, the anger of self-resembling faces. In turn, the happiness of non-resembling faces was less likely mimicked than in the case of self-resembling faces, while anger evoked a more divergent, smile-like response. Finally, we found that social evaluations were in general increased by happiness displays, but not influenced by resemblance. Overall, the study demonstrates an interesting and novel phenomenon, particularly that mimicry can be modified by relatively subtle cues of physical resemblance.

Spontaneous Facial Expressions and Micro-expressions Coding: From Brain to Face

Facial expressions are a vital way for humans to show their perceived emotions. It is convenient for detecting and recognizing expressions or micro-expressions by annotating a lot of data in deep learning. However, the study of video-based expressions or micro-expressions requires that coders have professional knowledge and be familiar with action unit (AU) coding, leading to considerable difficulties. This paper aims to alleviate this situation. We deconstruct facial muscle movements from the motor cortex and systematically sort out the relationship among facial muscles, AU, and emotion to make more people understand coding from the basic principles: We derived the relationship between AU and emotion based on a data-driven analysis of 5,000 images from the RAF-AU database, along with the experience of professional coders.We discussed the complex facial motor cortical network system that generates facial movement properties, detailing the facial nucleus and the motor system associated with facial expressions.The supporting physiological theory for AU labeling of emotions is obtained by adding facial muscle movements patterns.We present the detailed process of emotion labeling and the detection and recognition of AU.Based on the above research, the video's coding of spontaneous expressions and micro-expressions is concluded and prospected.

Face Reconstruction Based on Multiscale Feature Fusion and 3D Animation Design

The development of the Internet of Things and 3D technology promotes the wide application of face models in 3D animation. However, because the expression is inconsistent with the facial muscle movement, the reconstruction results may be far from the real appearance in the process of reconstructing the face appearance. Therefore, this paper proposes a character expression simulation model under the framework of 3DS Max. According to the relationship between head bones and muscles, a facial muscle motion model was established. Then, the expression simulation design of the original three-dimensional animation character “yaya” was carried out under the framework of 3DS Max technology. The experimental results of “yaya” facial expression test showed that the face simulation model using this method not only has vivid and natural expression but also conforms to the law of facial muscle movement, which provides an important reference for the construction and application of 3D face model.

Bilateral and Optimistic Warning Paradigms Improve the Predictive Power of Intraoperative Facial Motor Evoked Potentials during Vestibular Schwannoma Surgery

Facial muscle corticobulbar motor evoked potentials (FMcoMEPs) are used to monitor facial nerve integrity during vestibular schwannoma resections to increase maximal safe tumor resection. Established warning criteria, based on ipsilateral amplitude reduction, have the limitation that the rate of false positive alarms is high, in part because FMcoMEP changes occur on both sides, e.g., due to brain shift or pneumocephalus. We retrospectively compared the predictive value of ipsilateral-only warning criteria and actual intraoperative warnings with a novel candidate warning criterion, based on “ipsilateral versus contralateral difference in relative stimulation threshold increase, from baseline to end of resection” (BilatMT ≥ 20%), combined with an optimistic approach in which a warning would be triggered only if all facial muscles on the affected side deteriorated. We included 60 patients who underwent resection of vestibular schwannoma. The outcome variable was postoperative facial muscle function. Retrospectively applying BilatMT, with the optimistic approach, was found to have a significantly better false positive rate, which was much lower (9% at day 90) than the traditionally used ipsilateral warning criteria (>20%) and was also lower than actual intraoperative warnings. This is the first report combining the threshold method with an optimistic approach in a bilateral multi-facial muscle setup. This method could substantially reduce the rate of false positive alarms in FMcoMEP monitoring.

Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation

BACKGROUND: Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science. OBJECTIVE: In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy. METHOD: The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals. RESULTS: The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals. CONCLUSION: The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.

Synchrony in the periphery: inter-subject correlation of physiological responses during live music concerts

While there is an increasing shift in cognitive science to study perception of naturalistic stimuli, this study extends this goal to naturalistic contexts by assessing physiological synchrony across audience members in a concert setting. Cardiorespiratory, skin conductance, and facial muscle responses were measured from participants attending live string quintet performances of full-length works from Viennese Classical, Contemporary, and Romantic styles. The concert was repeated on three consecutive days with different audiences. Using inter-subject correlation (ISC) to identify reliable responses to music, we found that highly correlated responses depicted typical signatures of physiological arousal. By relating physiological ISC to quantitative values of music features, logistic regressions revealed that high physiological synchrony was consistently predicted by faster tempi (which had higher ratings of arousing emotions and engagement), but only in Classical and Romantic styles (rated as familiar) and not the Contemporary style (rated as unfamiliar). Additionally, highly synchronised responses across all three concert audiences occurred during important structural moments in the music—identified using music theoretical analysis—namely at transitional passages, boundaries, and phrase repetitions. Overall, our results show that specific music features induce similar physiological responses across audience members in a concert context, which are linked to arousal, engagement, and familiarity.

Smiling By Way of Zygomatic Electrical Stimulation: Investigating the Facial Feedback Hypothesis

While it is widely accepted that affective states precede facial expressions, the facial feedback hypothesis (FFH) proposes the inverse. The FFH postulates that facial muscle region activity (e.g., smiling or frowning) directly influences the experience of emotion. The purpose of the present study was to evaluate the validity of the FFH - specifically whether smiling independently enhances positive mood.

Objective and bias-free measures of candidate motivation during job applications

Society suffers from biases and discrimination, a longstanding dilemma that stems from ungrounded, subjective judgments. Especially unequal opportunities in labor remain a persistent challenge, despite the recent inauguration of top-down diplomatic measures. Here we propose a solution by using an objective approach to the measurement of nonverbal behaviors of job candidates that trained for a job assessment. First, we implemented and developed artificial intelligence, computer vision, and unbiased machine learning software to automatically detect facial muscle activity and emotional expressions to predict the candidates’ self-reported motivation levels. The motivation judgments by our model outperformed recruiters’ unreliable, invalid, and sometimes biased judgments. These findings mark the necessity and usefulness of novel, bias-free, and scientific approaches to candidate and employee screening and selection procedures in recruitment and human resources.