Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG.

Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective-physiological association.

Development of the RIKEN database for dynamic facial expressions with multiple angles.

The development of facial expressions with sensing information is progressing in multidisciplinary fields, such as psychology, affective computing, and cognitive science. Previous facial datasets have not simultaneously dealt with multiple theoretical views of emotion, individualized context, or multi-angle/depth information. We developed a new facial database (RIKEN facial expression database) that includes multiple theoretical views of emotions and expressers' individualized events with multi-angle and depth information. The RIKEN facial expression database contains recordings of 48 Japanese participants captured using ten Kinect cameras at 25 events. This study identified several valence-related facial patterns and found them consistent with previous research investigating the coherence between facial movements and internal states. This database represents an advancement in developing a new sensing system, conducting psychological experiments, and understanding the complexity of emotional events.

Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases.

In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.