AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review

Death by suicide is the seventh leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI applications in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of research that applies AI to extract audiovisual non-verbal cues for mental illness assessment. However, the majority of the recent works focus on depression, despite the evident difference between depression symptoms and suicidal behavior and non-verbal cues. This paper reviews recent works that study suicide ideation and suicide behavior detection through audiovisual feature analysis, mainly suicidal voice/speech acoustic features analysis and suicidal visual cues. Automatic suicide assessment is a promising research direction that is still in the early stages. Accordingly, there is a lack of large datasets that can be used to train machine learning and deep learning models proven to be effective in other, similar tasks.Comment: Manuscript submitted to Arificial Intelligence Reviews (2022

Looking at the Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the limited available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. To enable this research, we have collected and analyzed a new dataset containing full body videos for short interviews and self-reported distress labels. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We perform analysis on statistical body gestures and fidgeting features to explore how distress levels affect participants' behaviors. We then propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector Encoding. We demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels

What does not happen: quantifying embodied engagement using NIMI and self-adaptors

Previous research into the quantification of embodied intellectual and emotional engagement using non-verbal movement parameters has not yielded consistent results across different studies. Our research introduces NIMI (Non-Instrumental Movement Inhibition) as an alternative parameter. We propose that the absence of certain types of possible movements can be a more holistic proxy for cognitive engagement with media (in seated persons) than searching for the presence of other movements. Rather than analyzing total movement as an indicator of engagement, our research team distinguishes between instrumental movements (i.e. physical movement serving a direct purpose in the given situation) and non-instrumental movements, and investigates them in the context of the narrative rhythm of the stimulus. We demonstrate that NIMI occurs by showing viewers’ movement levels entrained (i.e. synchronised) to the repeating narrative rhythm of a timed computer-presented quiz. Finally, we discuss the role of objective metrics of engagement in future context-aware analysis of human behaviour in audience research, interactive media and responsive system and interface design

Automatic Detection of Self-Adaptors for Psychological Distress

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the lack of available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We also propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector encoding. We also demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels. To enable this research we introduce a new dataset containing full body videos for short interviews and self-reported distress labels.King's College, Cmabridg

Video Based Deep CNN Model for Depression Detection

Our face reflects our feelings towards anything and everything we see, smell, teste or feel through any of our senses. Hence multiple attempts have been made since last few decades towards understanding the facial expressions. Emotion detection has numerous applications since Safe Driving, Health Monitoring Systems, Marketing and Advertising etc. We propose an Automatic Depression Detection (ADD) system based on Facial Expression Recognition (FER). We propose a model to optimize the FER system for understanding seven basic emotions (joy, sadness, fear, anger, surprise, disgust and neutral) and use it for detection of Depression Level in the subject. The proposed model will detect if a person is in depression and if so, up to what extent. Our model will be based on a Deep Convolution Neural Network (DCNN)