Automatic depression scale prediction using facial expression dynamics and regression

Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behaviour, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.The work by Asim Jan was supported by School of Engineering & Design/Thomas Gerald Gray PGR Scholarship. The work by Hongying Meng and Saeed Turabzadeh was partially funded by the award of the Brunel Research Initiative and Enterprise Fund (BRIEF). The work by Yona Falinie Binti Abd Gaus was supported by Majlis Amanah Rakyat (MARA) Scholarship

Deficient auditory emotion processing but intact emotional multisensory integration in alexithymia

Alexithymia has been associated with emotion recognition deficits in both auditory and visual domains. Although emotions are inherently multimodal in daily life, little is known regarding abnormalities of emotional multisensory integration (eMSI) in relation to alexithymia. Here, we employed an emotional Stroop-like audiovisual task while recording event-related potentials (ERPs) in individuals with high alexithymia levels (HA) and low alexithymia levels (LA). During the task, participants had to indicate whether a voice was spoken in a sad or angry prosody while ignoring the simultaneously presented static face which could be either emotionally congruent or incongruent to the human voice. We found that HA performed worse and showed higher P2 amplitudes than LA independent of emotion congruency. Furthermore, difficulties in identifying and describing feelings were positively correlated with the P2 component, and P2 correlated negatively with behavioral performance. Bayesian statistics showed no group differences in eMSI and classical integration-related ERP components (N1 and N2). Although individuals with alexithymia indeed showed deficits in auditory emotion recognition as indexed by decreased performance and higher P2 amplitudes, the present findings suggest an intact capacity to integrate emotional information from multiple channels in alexithymia. Our work provides valuable insights into the relationship between alexithymia and neuropsychological mechanisms of emotional multisensory integration