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

Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201

CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and Cognitive Theory

Active safety systems on vehicles often face problems with false alarms. Most active safety systems predict the driver's trajectory with the assumption that the driver is always in a normal emotion, and then infer risks. However, the driver's trajectory uncertainty increases under abnormal emotions. This paper proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle trajectories under abnormal emotions. At the physical level, the interaction features between vehicles are extracted by the physical GCN module. At the cognitive level, SOR cognitive theory is used as prior knowledge to build a Dynamic Bayesian Network (DBN) structure. The conditional probability and state transition probability of nodes from the calibrated SOR-DBN quantify the causal relationship between cognitive factors, which is embedded into the cognitive GCN module to extract the characteristics of the influence mechanism of emotions on driving behavior. The CARLA-SUMO joint driving simulation platform was built to develop dangerous pre-crash scenarios. Methods of recreating traffic scenes were used to naturally induce abnormal emotions. The experiment collected data from 26 participants to verify the proposed model. Compared with the model that only considers physical motion features, the prediction accuracy of the proposed model is increased by 68.70%. Furthermore,considering the SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with other advanced trajectory prediction models, the results of CPSOR-GCN also have lower errors. This model can be integrated into active safety systems to better adapt to the driver's emotions, which could effectively reduce false alarms.Comment: 15 pages, 31 figures, submitted to IEEE Transactions on Intelligent Vehicle

Conceptual metaphor and spatial representations of time : the role of affect

Conceptual metaphor involves understanding abstract concepts (e.g., time) in terms of more concrete bodily experiences (e.g., spatial location and movement). Research has identified two different spatio-temporal metaphorical perspectives on time, as reflected in the contrast between ”Christmas is coming” and “We are approaching Christmas”. It has been found that which perspective is chosen can depend on the perceiver’s situation and experience. Four recent studies (Hauser, Carter, & Meier, 2009; Lee & Ji, 2014; Margolies & Crawford, 2008; Richmond, Clare Wilson, & Zinken, 2012) examined the role of emotion on choice of temporal perspective. The current project sought to address the anomalous results and several key issues arising from those studies. First, a series of critical questions were developed and discussed from interrogating the wider research literature on the two spatio-temporal metaphors and from conducting a research synthesis that examined methodological and statistical issues in that wider literature. This was followed by two experiments. The first experiment tested which of two emotion-induction methods, text or film, would be more effective. The second experiment examined the effect of induced emotion (via text) and event valence on choice of spatio-temporal metaphor. Participants (n = 504) were randomly assigned to one of nine experimental conditions, each participant having either a positive, negative, or neutral emotion induced and responding about an event that was either positive, negative or neutral. Additional measures were taken of trait test anxiety, social anxiety, and more general negative emotional states. Emotion induction was effective and there was a significant difference in some responses for traits and for more general negative emotional states. No other significant differences were found. The combined results of the literature interrogation, research synthesis, and experiments are discussed in light of the changing climate in psychology favouring a broader approach to science that includes conceptual analysis, null results, and replications. It is argued that the project has highlighted a previously unacknowledged relationship between emotion, event valence, and temporal perspective, and has revealed a general misunderstanding regarding the interpretation of responses on the standard measures used. This suggests redirection along more fruitful lines of future research into the effect of emotion on choice of spatio-temporal metaphor