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

Public mental health through social media in the post COVID-19 era

Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status. This situation warrants to automate the detection of mental disorders. This paper presents a methodology for the detection of mental disorders using micro facial expressions. Micro-expressions are momentary, involuntary facial expressions that can be indicative of deeper feelings and mental states. Nevertheless, manually detecting and interpreting micro-expressions can be rather challenging. A deep learning HybridMicroNet model, based on convolution neural networks, is proposed for emotion recognition from micro-expressions. Further, a case study for the detection of mental health has been undertaken. The findings demonstrated that the proposed model achieved a high accuracy when attempting to diagnose mental health disorders based on micro-expressions. The attained accuracy on the CASME dataset was 99.08%, whereas the accuracy that was achieved on SAMM dataset was 97.62%. Based on these findings, deep learning may prove to be an effective method for diagnosing mental health conditions by analyzing micro-expressions

A facial depression recognition method based on hybrid multi-head cross attention network

IntroductionDeep-learn methods based on convolutional neural networks (CNNs) have demonstrated impressive performance in depression analysis. Nevertheless, some critical challenges need to be resolved in these methods: (1) It is still difficult for CNNs to learn long-range inductive biases in the low-level feature extraction of different facial regions because of the spatial locality. (2) It is difficult for a model with only a single attention head to concentrate on various parts of the face simultaneously, leading to less sensitivity to other important facial regions associated with depression. In the case of facial depression recognition, many of the clues come from a few areas of the face simultaneously, e.g., the mouth and eyes.MethodsTo address these issues, we present an end-to-end integrated framework called Hybrid Multi-head Cross Attention Network (HMHN), which includes two stages. The first stage consists of the Grid-Wise Attention block (GWA) and Deep Feature Fusion block (DFF) for the low-level visual depression feature learning. In the second stage, we obtain the global representation by encoding high-order interactions among local features with Multi-head Cross Attention block (MAB) and Attention Fusion block (AFB).ResultsWe experimented on AVEC2013 and AVEC2014 depression datasets. The results of AVEC 2013 (RMSE = 7.38, MAE = 6.05) and AVEC 2014 (RMSE = 7.60, MAE = 6.01) demonstrated the efficacy of our method and outperformed most of the state-of-the-art video-based depression recognition approaches.DiscussionWe proposed a deep learning hybrid model for depression recognition by capturing the higher-order interactions between the depression features of multiple facial regions, which can effectively reduce the error in depression recognition and gives great potential for clinical experiments

Multi-Modality Human Action Recognition

Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model

Multimodal sentiment analysis in real-life videos

This thesis extends the emerging field of multimodal sentiment analysis of real-life videos, taking two components into consideration: the emotion and the emotion's target. The emotion component of media is traditionally represented as a segment-based intensity model of emotion classes. This representation is replaced here by a value- and time-continuous view. Adjacent research fields, such as affective computing, have largely neglected the linguistic information available from automatic transcripts of audio-video material. As is demonstrated here, this text modality is well-suited for time- and value-continuous prediction. Moreover, source-specific problems, such as trustworthiness, have been largely unexplored so far. This work examines perceived trustworthiness of the source, and its quantification, in user-generated video data and presents a possible modelling path. Furthermore, the transfer between the continuous and discrete emotion representations is explored in order to summarise the emotional context at a segment level. The other component deals with the target of the emotion, for example, the topic the speaker is addressing. Emotion targets in a video dataset can, as is shown here, be coherently extracted based on automatic transcripts without limiting a priori parameters, such as the expected number of targets. Furthermore, alternatives to purely linguistic investigation in predicting targets, such as knowledge-bases and multimodal systems, are investigated. A new dataset is designed for this investigation, and, in conjunction with proposed novel deep neural networks, extensive experiments are conducted to explore the components described above. The developed systems show robust prediction results and demonstrate strengths of the respective modalities, feature sets, and modelling techniques. Finally, foundations are laid for cross-modal information prediction systems with applications to the correction of corrupted in-the-wild signals from real-life videos

Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition