Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer

Affect recognition & generation in-the-wild

Affect recognition based on a subject’s facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This Ph.D. Thesis exploits these advances and makes significant contributions for affect analysis and recognition in-the-wild. We tackle affect analysis and recognition as a dual knowledge generation problem: i) we create new, large and rich in-the-wild databases and ii) we design and train novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets. At first, we present the creation of Aff-Wild database annotated according to valence-arousal and an end-to-end CNN-RNN architecture, AffWildNet. Then we use AffWildNet as a robust prior for dimensional and categorical affect recognition and extend it by extracting low-/mid-/high-level latent information and analysing this via multiple RNNs. Additionally, we propose a novel loss function for DNN-based categorical affect recognition. Next, we generate Aff-Wild2, the first database containing annotations for all main behavior tasks: estimate Valence-Arousal; classify into Basic Expressions; detect Action Units. We develop multi-task and multi-modal extensions of AffWildNet by fusing these tasks and propose a novel holistic approach that utilises all existing databases with non-overlapping annotations and couples them through co-annotation and distribution matching. Finally, we present an approach for valence-arousal, or basic expressions’ facial affect synthesis. We generate an image with a given affect, or a sequence of images with evolving affect, by annotating a 4-D database and utilising a 3-D morphable model.Open Acces

Investigating multi-modal features for continuous affect recognition using visual sensing

Emotion plays an essential role in human cognition, perception and rational decisionmaking. In the information age, people spend more time then ever before interacting with computers, however current technologies such as Artificial Intelligence (AI) and Human-Computer Interaction (HCI) have largely ignored the implicit information of a user’s emotional state leading to an often frustrating and cold user experience. To bridge this gap between human and computer, the field of affective computing has become a popular research topic. Affective computing is an interdisciplinary field encompassing computer, social, cognitive, psychology and neural science. This thesis focuses on human affect recognition, which is one of the most commonly investigated areas in affective computing. Although from a psychology point of view, emotion is usually defined differently from affect, for this thesis the terms emotion, affect, emotional state and affective state are used interchangeably. Both visual and vocal cues have been used in previous research to recognise a human’s affective states. For visual cues, information from the face is often used. Although these systems achieved good performance under laboratory settings, it has proved a challenging task to translate these to unconstrained environments due to variations in head pose and lighting conditions. Since a human face is a threedimensional (3D) object whose 2D projection is sensitive to the aforementioned variations, recent trends have shifted towards using 3D facial information to improve the accuracy and robustness of the systems. However these systems are still focused on recognising deliberately displayed affective states, mainly prototypical expressions of six basic emotions (happiness, sadness, fear, anger, surprise and disgust). To our best knowledge, no research has been conducted towards continuous recognition of spontaneous affective states using 3D facial information. The main goal of this thesis is to investigate the use of 2D (colour) and 3D (depth) facial information to recognise spontaneous affective states continuously. Due to a lack of an existing continuous annotated spontaneous data set, which contains both colour and depth information, such a data set was created. To better understand the processes in affect recognition and to compare results of the proposed methods, a baseline system was implemented. Then the use of colour and depth information for affect recognition were examined separately. For colour information, an investigation was carried out to explore the performance of various state-of-art 2D facial features using different publicly available data sets as well as the captured data set. Experiments were also carried out to study if it is possible to predict a human’s affective state using 2D features extracted from individual facial parts (E.g. eyes and mouth). For depth information, a number of histogram based features were used and their performance was evaluated. Finally a multi-modal affect recognition framework utilising both colour and depth information is proposed and its performance was evaluated using the captured data set