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

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes

Modelling person-specific and multi-scale facial dynamics for automatic personality and depression analysis

‘To know oneself is true progress’. While one's identity is difficult to be fully described, a key part of it is one’s personality. Accurately understanding personality can benefit various aspects of human's life. There is convergent evidence suggesting that personality traits are marked by non-verbal facial expressions of emotions, which in theory means that automatic personality assessment is possible from facial behaviours. Thus, this thesis aims to develop video-based automatic personality analysis approaches. Specifically, two video-level dynamic facial behaviour representations are proposed for automatic personality traits estimation, namely person-specific representation and spectral representation, which focus on addressing three issues that have been frequently occurred in existing automatic personality analysis approaches: 1. attempting to use super short video segments or even a single frame to infer personality traits; 2. lack of proper way to retain multi-scale long-term temporal information; 3. lack of methods to encode person-specific facial dynamics that are relatively stable over time but differ across individuals. This thesis starts with extending the dynamic image algorithm to modeling preceding and succeeding short-term face dynamics of each frame in a video, which achieved good performance in estimating valence/arousal intensities, showing good dynamic encoding ability of such dynamic representation. This thesis then proposes a novel Rank Loss, aiming to train a network that produces similar dynamic representation per-frame but only from a still image. This way, the network can learn generic facial dynamics from unlabelled face videos in a self-supervised manner. Based on such an approach, the person-specific representation encoding approach is proposed. It firstly freezes the well-trained generic network, and incorporates a set of intermediate filters, which are trained again but with only person-specific videos based on the same self-supervised learning approach. As a result, the learned filters' weights are person-specific, and can be concatenated as a 1-D video-level person-specific representation. Meanwhile, this thesis also proposes a spectral analysis approach to retain multi-scale video-level facial dynamics. This approach uses automatically detected human behaviour primitives as the low-dimensional descriptor for each frame, and converts long and variable-length time-series behaviour signals to small and length-independent spectral representations to represent video-level multi-scale temporal dynamics of expressive behaviours. Consequently, the combination of two representations, which contains not only multi-scale video-level facial dynamics but also person-specific video-level facial dynamics, can be applied to automatic personality estimation. This thesis conducts a series of experiments to validate the proposed approaches: 1. the arousal/valence intensity estimation is conducted on both a controlled face video dataset (SEMAINE) and a wild face video dataset (Affwild-2), to evaluate the dynamic encoding capability of the proposed Rank Loss; 2. the proposed automatic personality traits recognition systems (spectral representation and person-specific representation) are evaluated on face video datasets that labelled with either 'Big-Five' apparent personality traits (ChaLearn) or self-reported personality traits (VHQ); 3. the depression studies are also evaluated on the VHQ dataset that is labelled with PHQ-9 depression scores. The experimental results on automatic personality traits and depression severity estimation tasks show the person-specific representation's good performance in personality task and spectral vector's superior performance in depression task. In particular, the proposed person-specific approach achieved a similar performance to the state-of-the-art method in apparent personality traits recognition task and achieved at least 15% PCC improvements over other approaches in self-reported personality traits recognition task. Meanwhile, the proposed spectral representation shows better performance than the person-specific approach in depression severity estimation task. In addition, this thesis also found that adding personality traits labels/predictions into behaviour descriptors improved depression severity estimation results

Modelling person-specific and multi-scale facial dynamics for automatic personality and depression analysis

‘To know oneself is true progress’. While one's identity is difficult to be fully described, a key part of it is one’s personality. Accurately understanding personality can benefit various aspects of human's life. There is convergent evidence suggesting that personality traits are marked by non-verbal facial expressions of emotions, which in theory means that automatic personality assessment is possible from facial behaviours. Thus, this thesis aims to develop video-based automatic personality analysis approaches. Specifically, two video-level dynamic facial behaviour representations are proposed for automatic personality traits estimation, namely person-specific representation and spectral representation, which focus on addressing three issues that have been frequently occurred in existing automatic personality analysis approaches: 1. attempting to use super short video segments or even a single frame to infer personality traits; 2. lack of proper way to retain multi-scale long-term temporal information; 3. lack of methods to encode person-specific facial dynamics that are relatively stable over time but differ across individuals. This thesis starts with extending the dynamic image algorithm to modeling preceding and succeeding short-term face dynamics of each frame in a video, which achieved good performance in estimating valence/arousal intensities, showing good dynamic encoding ability of such dynamic representation. This thesis then proposes a novel Rank Loss, aiming to train a network that produces similar dynamic representation per-frame but only from a still image. This way, the network can learn generic facial dynamics from unlabelled face videos in a self-supervised manner. Based on such an approach, the person-specific representation encoding approach is proposed. It firstly freezes the well-trained generic network, and incorporates a set of intermediate filters, which are trained again but with only person-specific videos based on the same self-supervised learning approach. As a result, the learned filters' weights are person-specific, and can be concatenated as a 1-D video-level person-specific representation. Meanwhile, this thesis also proposes a spectral analysis approach to retain multi-scale video-level facial dynamics. This approach uses automatically detected human behaviour primitives as the low-dimensional descriptor for each frame, and converts long and variable-length time-series behaviour signals to small and length-independent spectral representations to represent video-level multi-scale temporal dynamics of expressive behaviours. Consequently, the combination of two representations, which contains not only multi-scale video-level facial dynamics but also person-specific video-level facial dynamics, can be applied to automatic personality estimation. This thesis conducts a series of experiments to validate the proposed approaches: 1. the arousal/valence intensity estimation is conducted on both a controlled face video dataset (SEMAINE) and a wild face video dataset (Affwild-2), to evaluate the dynamic encoding capability of the proposed Rank Loss; 2. the proposed automatic personality traits recognition systems (spectral representation and person-specific representation) are evaluated on face video datasets that labelled with either 'Big-Five' apparent personality traits (ChaLearn) or self-reported personality traits (VHQ); 3. the depression studies are also evaluated on the VHQ dataset that is labelled with PHQ-9 depression scores. The experimental results on automatic personality traits and depression severity estimation tasks show the person-specific representation's good performance in personality task and spectral vector's superior performance in depression task. In particular, the proposed person-specific approach achieved a similar performance to the state-of-the-art method in apparent personality traits recognition task and achieved at least 15% PCC improvements over other approaches in self-reported personality traits recognition task. Meanwhile, the proposed spectral representation shows better performance than the person-specific approach in depression severity estimation task. In addition, this thesis also found that adding personality traits labels/predictions into behaviour descriptors improved depression severity estimation results

Towards Video Transformers for Automatic Human Analysis

[eng] With the aim of creating artificial systems capable of mirroring the nuanced understanding and interpretative powers inherent to human cognition, this thesis embarks on an exploration of the intersection between human analysis and Video Transformers. The objective is to harness the potential of Transformers, a promising architectural paradigm, to comprehend the intricacies of human interaction, thus paving the way for the development of empathetic and context-aware intelligent systems. In order to do so, we explore the whole Computer Vision pipeline, from data gathering, to deeply analyzing recent developments, through model design and experimentation. Central to this study is the creation of UDIVA, an expansive multi-modal, multi-view dataset capturing dyadic face-to-face human interactions. Comprising 147 participants across 188 sessions, UDIVA integrates audio-visual recordings, heart-rate measurements, personality assessments, socio- demographic metadata, and conversational transcripts, establishing itself as the largest dataset for dyadic human interaction analysis up to this date. This dataset provides a rich context for probing the capabilities of Transformers within complex environments. In order to validate its utility, as well as to elucidate Transformers' ability to assimilate diverse contextual cues, we focus on addressing the challenge of personality regression within interaction scenarios. We first adapt an existing Video Transformer to handle multiple contextual sources and conduct rigorous experimentation. We empirically observe a progressive enhancement in model performance as more context is added, reinforcing the potential of Transformers to decode intricate human dynamics. Building upon these findings, the Dyadformer emerges as a novel architecture, adept at long-range modeling of dyadic interactions. By jointly modeling both participants in the interaction, as well as embedding multi- modal integration into the model itself, the Dyadformer surpasses the baseline and other concurrent approaches, underscoring Transformers' aptitude in deciphering multifaceted, noisy, and challenging tasks such as the analysis of human personality in interaction. Nonetheless, these experiments unveil the ubiquitous challenges when training Transformers, particularly in managing overfitting due to their demand for extensive datasets. Consequently, we conclude this thesis with a comprehensive investigation into Video Transformers, analyzing topics ranging from architectural designs and training strategies, to input embedding and tokenization, traversing through multi-modality and specific applications. Across these, we highlight trends which optimally harness spatio-temporal representations that handle video redundancy and high dimensionality. A culminating performance comparison is conducted in the realm of video action classification, spotlighting strategies that exhibit superior efficacy, even compared to traditional CNN-based methods.[cat] Aquesta tesi busca crear sistemes artificials que reflecteixin les habilitats de comprensió i interpretació humanes a través de l'ús de Transformers per a vídeo. L'objectiu és utilitzar aquestes arquitectures per comprendre millor la interacció humana i desenvolupar sistemes intel·ligents i conscients de l'entorn. Això implica explorar àmplies àrees de la Visió per Computador, des de la recopilació de dades fins a l'anàlisi de l'estat de l'art i la prova experimental d'aquests models. Una part essencial d'aquest estudi és la creació d'UDIVA, un ampli conjunt de dades multimodal i multivista que enregistra interaccions humanes cara a cara. Amb 147 participants i 188 sessions, UDIVA inclou contingut audiovisual, freqüència cardíaca, perfils de personalitat, dades sociodemogràfiques i transcripcions de les converses. És el conjunt de dades més gran conegut per a l'anàlisi de la interacció humana diàdica i proporciona un context ric per a l'estudi de les capacitats dels Transformers en entorns complexos. Per tal de validar la seva utilitat i les habilitats dels Transformers, ens centrem en la regressió de la personalitat. Inicialment, adaptem un Transformer de vídeo per integrar diverses fonts de context. Mitjançant experiments exhaustius, observem millores progressives en els resultats amb la inclusió de més context, confirmant la capacitat dels Transformers. Motivats per aquests resultats, desenvolupem el Dyadformer, una arquitectura per interaccions diàdiques de llarga duració. Aquesta nova arquitectura considera simultàniament els dos participants en la interacció i incorpora la multimodalitat en un sol model. El Dyadformer supera la nostra proposta inicial i altres treballs similars, destacant la capacitat dels Transformers per abordar tasques complexes. No obstant això, aquestos experiments revelen reptes d'entrenament dels Transformers, com el sobreajustament, per la seva necessitat de grans conjunts de dades. La tesi conclou amb una anàlisi profunda dels Transformers per a vídeo, incloent dissenys arquitectònics, estratègies d'entrenament, preprocessament de vídeos, tokenització i multimodalitat. S'identifiquen tendències per gestionar la redundància i alta dimensionalitat de vídeos i es realitza una comparació de rendiment en la classificació d'accions a vídeo, destacant estratègies d'eficàcia superior als mètodes tradicionals basats en convolucions

A review of affective computing: From unimodal analysis to multimodal fusion

Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the proliferation of videos posted online (e.g., on YouTube, Facebook, Twitter) for product reviews, movie reviews, political views, and more, affective computing research has increasingly evolved from conventional unimodal analysis to more complex forms of multimodal analysis. This is the primary motivation behind our first of its kind, comprehensive literature review of the diverse field of affective computing. Furthermore, existing literature surveys lack a detailed discussion of state of the art in multimodal affect analysis frameworks, which this review aims to address. Multimodality is defined by the presence of more than one modality or channel, e.g., visual, audio, text, gestures, and eye gage. In this paper, we focus mainly on the use of audio, visual and text information for multimodal affect analysis, since around 90% of the relevant literature appears to cover these three modalities. Following an overview of different techniques for unimodal affect analysis, we outline existing methods for fusing information from different modalities. As part of this review, we carry out an extensive study of different categories of state-of-the-art fusion techniques, followed by a critical analysis of potential performance improvements with multimodal analysis compared to unimodal analysis. A comprehensive overview of these two complementary fields aims to form the building blocks for readers, to better understand this challenging and exciting research field

Exploiting Group Structures to Infer Social Interactions From Videos

In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features and a novel class of meta-features we callLiarRank. We develop a LiarOrNot model to identify spies in Resistance videos. We achieve AUCs of over 0.70 outperforming our baselines by 3% and human judges by 12%. For the problem of political deception, we first collect a dataset of videos and transcripts of 76 politicians from 18 countries making truthful and deceptive statements. We call it the Global Political Deception Dataset. We then show how to analyze the statements in a broader context by building a Video-Article-Topic graph. From this graph, we create a novel class of features called Deception Score that captures how controversial each topic is and how it affects the truthfulness of each statement. We show that our approach achieves 0.775 AUC outperforming competing baselines. Finally, we use the Resistance data to solve the problem of dyadic impression prediction. Our proposed Dyadic Impression Prediction System (DIPS) contains four major innovations: a novel class of features called emotion ranks, sign imbalance features derived from signed graphs theory, a novel method to align the facial expressions of subjects, and finally, we propose the concept of a multilayered stochastic network we call Temporal Delayed Network. Our DIPS architecture beats eight baselines from the literature, yielding statistically significant improvements of 19.9-30.8% in AUC