3D Face Modelling, Analysis and Synthesis

Human faces have always been of a special interest to researchers in the computer vision and graphics areas. There has been an explosion in the number of studies around accurately modelling, analysing and synthesising realistic faces for various applications. The importance of human faces emerges from the fact that they are invaluable means of effective communication, recognition, behaviour analysis, conveying emotions, etc. Therefore, addressing the automatic visual perception of human faces efficiently could open up many influential applications in various domains, e.g. virtual/augmented reality, computer-aided surgeries, security and surveillance, entertainment, and many more. However, the vast variability associated with the geometry and appearance of human faces captured in unconstrained videos and images renders their automatic analysis and understanding very challenging even today. The primary objective of this thesis is to develop novel methodologies of 3D computer vision for human faces that go beyond the state of the art and achieve unprecedented quality and robustness. In more detail, this thesis advances the state of the art in 3D facial shape reconstruction and tracking, fine-grained 3D facial motion estimation, expression recognition and facial synthesis with the aid of 3D face modelling. We give a special attention to the case where the input comes from monocular imagery data captured under uncontrolled settings, a.k.a. \textit{in-the-wild} data. This kind of data are available in abundance nowadays on the internet. Analysing these data pushes the boundaries of currently available computer vision algorithms and opens up many new crucial applications in the industry. We define the four targeted vision problems (3D facial reconstruction $\&$ tracking, fine-grained 3D facial motion estimation, expression recognition, facial synthesis) in this thesis as the four 3D-based essential systems for the automatic facial behaviour understanding and show how they rely on each other. Finally, to aid the research conducted in this thesis, we collect and annotate a large-scale videos dataset of monocular facial performances. All of our proposed methods demonstarte very promising quantitative and qualitative results when compared to the state-of-the-art methods

Towards spatial and temporal analysis of facial expressions in 3D data

Facial expressions are one of the most important means for communication of emotions and meaning. They are used to clarify and give emphasis, to express intentions, and form a crucial part of any human interaction. The ability to automatically recognise and analyse expressions could therefore prove to be vital in human behaviour understanding, which has applications in a number of areas such as psychology, medicine and security. 3D and 4D (3D+time) facial expression analysis is an expanding field, providing the ability to deal with problems inherent to 2D images, such as out-of-plane motion, head pose, and lighting and illumination issues. Analysis of data of this kind requires extending successful approaches applied to the 2D problem, as well as the development of new techniques. The introduction of recent new databases containing appropriate expression data, recorded in 3D or 4D, has allowed research into this exciting area for the first time. This thesis develops a number of techniques, both in 2D and 3D, that build towards a complete system for analysis of 4D expressions. Suitable feature types, designed by employing binary pattern methods, are developed for analysis of 3D facial geometry data. The full dynamics of 4D expressions are modelled, through a system reliant on motion-based features, to demonstrate how the different components of the expression (neutral-onset-apex-offset) can be distinguished and harnessed. Further, the spatial structure of expressions is harnessed to improve expression component intensity estimation in 2D videos. Finally, it is discussed how this latter step could be extended to 3D facial expression analysis, and also combined with temporal analysis. Thus, it is demonstrated that both spatial and temporal information, when combined with appropriate 3D features, is critical in analysis of 4D expression data.Open Acces

Using data visualization to deduce faces expressions

Conferência Internacional, realizada na Turquia, de 6-8 de setembro de 2018.Collect and examine in real time multi modal sensor data of a human face, is an important problem in computer vision, with applications in medical and monitoring analysis, entertainment and security. Although its advances, there are still many open issues in terms of the identification of the facial expression. Different algorithms and approaches have been developed to find out patterns and characteristics that can help the automatic expression identification. One way to study data is through data visualizations. Data visualization turns numbers and letters into aesthetically pleasing visuals, making it easy to recognize patterns and find exceptions. In this article, we use information visualization as a tool to analyse data points and find out possible existing patterns in four different facial expressions.info:eu-repo/semantics/publishedVersio

Machine Analysis of Facial Expressions

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An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data

Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). An approach build on discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Such mechanism can yield a platform for objective and effortless assessment of human activity within the context of serious gaming and human-computer interaction.Comment: 8th Computer Science and Electronic Engineering, (CEEC 2016), University of Essex, UK, 6 page

Towards the improvement of self-service systems via emotional virtual agents

Affective computing and emotional agents have been found to have a positive effect on human-computer interactions. In order to develop an acceptable emotional agent for use in a self-service interaction, two stages of research were identified and carried out; the first to determine which facial expressions are present in such an interaction and the second to determine which emotional agent behaviours are perceived as appropriate during a problematic self-service shopping task. In the first stage, facial expressions associated with negative affect were found to occur during self-service shopping interactions, indicating that facial expression detection is suitable for detecting negative affective states during self-service interactions. In the second stage, user perceptions of the emotional facial expressions displayed by an emotional agent during a problematic self-service interaction were gathered. Overall, the expression of disgust was found to be perceived as inappropriate while emotionally neutral behaviour was perceived as appropriate, however gender differences suggested that females perceived surprise as inappropriate. Results suggest that agents should change their behaviour and appearance based on user characteristics such as gender