Yes / Understanding the detailed differences between posed and spontaneous smiles is an important topic with a range of applications such as in human-computer interaction, automatic facial emotion analysis and in awareness systems. During the past decade or so, there have been very promising solutions for accurate automatic recognition and detailed facial emotion analysis. To this end, many methods and techniques have been proposed for distinguishing between spontaneous and posed smiles. Our aim here is to go beyond the present state of the art in this field. Hence, in this work, we are concerned with understanding the exact distribution of a smile – both spontaneous and posed – across the face. To do this, we utilise a lightweight computational framework which we have developed to analyse the dynamics of human facial expressions. We utilise this framework to undertake a detailed study of the smile expression. Based on computing the optical flow across the face – especially across key parts of the face such as the mouth, the cheeks and around the eyes – we are able to accurately map the dynamic weight distribution of the smile expression. To validate our computational model, we utilise two publicly available datasets, namely the CK + dataset in which the subjects express posed smiles and the MUG dataset in which the subjects express genuine smiles. Our results not only confirm what already exists in the literature – i.e. that the spontaneous genuine smile is truly in the eyes – but it also gives further insight into the exact distribution of the smile across the face.

No / This chapter presents an application of computational smile analysis framework discussed earlier. Here we discuss how one could utilise a computational algorithm to distinguish between genuine and posed smiles. We utilise aspects of the computational framework discussed in Chap. 2 to process and analyse the smile expression looking for clues to determine the genuineness of it. Equally, we discuss how the exact distribution of a smile across the face, especially the distinction in the weight distribution between a genuine and a posed smile can be achieved.

Facial expressions are a useful source of information about the emotional state of others. However, facial expressions do not always correspond with an underlying emotional state. It is advantageous for perceivers to be able to differentiate between those expressions that are associated with a corresponding emotional state (genuine expressions) and those which are not associated with underlying emotions (posed expressions). The present study investigated the sensitivity of children with Autism Spectrum Disorder (ASD), and age and sex-matched control children to the different emotions underlying posed and genuine smiles. The first task required participants to listen to 12 emotion eliciting stories and select, from a grid of 4 facial expressions (a genuine smile, a posed smile, a neutral expression and a sad expression) that which matched how the target in the story would feel. Children with ASD correctly matched facial expressions and stories than did participants without ASD. The second task required children to look at a series of faces, each displaying either a posed smile, a genuine smile or a neutral expression and indicate whether each target was or was not happy. Participants with ASD were less sensitive both to the underlying emotional state of the targets and to the difference between posed and genuine smiles than were the control participants. Results are discussed in terms of the social deficits symptomatic of ASD.

Facial expressions are a useful source of information about the emotional state of others. However, facial expressions do not always correspond with an underlying emotional state. It is advantageous for perceivers to be able to differentiate between those expressions that are associated with a corresponding emotional state (genuine expressions) and those which are not associated with underlying emotions (posed expressions). The present study investigated the sensitivity of children with Autism Spectrum Disorder (ASD), and age and sex-matched control children to the different emotions underlying posed and genuine smiles. The first task required participants to listen to 12 emotion eliciting stories and select, from a grid of 4 facial expressions (a genuine smile, a posed smile, a neutral expression and a sad expression) that which matched how the target in the story would feel. Children with ASD correctly matched facial expressions and stories than did participants without ASD. The second task required children to look at a series of faces, each displaying either a posed smile, a genuine smile or a neutral expression and indicate whether each target was or was not happy. Participants with ASD were less sensitive both to the underlying emotional state of the targets and to the difference between posed and genuine smiles than were the control participants. Results are discussed in terms of the social deficits symptomatic of ASD.

no / The requirement to efficiently store and process SMILES data used in Chemoinformatics creates a demand for efficient techniques to compress this data. General-purpose transforms and compressors are available to transform and compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. We develop a transform specific to SMILES data that can be used alongside other general-purpose compressors as a preprocessor and post-processor to improve the compression of SMILES data. We test our transform with six other general-purpose compressors and also compare our results with another transform on our SMILES data corpus, we also compare our results with untransformed data.

Une nouvelle synthèse d'isonitrile à partir de cyanure d'argent a été mise au point, et utilisée pour des réactions multicomposants en un seul pot. Parallèlement à ces travaux, une cascade de réarrangements de Smiles a été étudiée, permettant d'aboutir en deux étapes à des composés cycliques à haute valeur ajoutée.

University of Macau / Faculty of Arts and Humanities. / Department of English

De nouvelle réactions multicomposants ont été mises au point. Une nouvelle ouverture de furane a par ailleurs été développée.

In many applications, such as conversational agents, virtual reality, movies, and games, animated facial expressions of computer-generated (CG) characters are used to communicate, teach, or entertain. With an increased demand for CG characters, it is important to animate accurate, realistic facial expressions because human facial expressions communicate a wealth of information. However, realistically animating faces is challenging and time-consuming for two reasons. First, human observers are adept at detecting anomalies in realistic CG facial animations. Second, traditional animation techniques based on keyframing sometimes approximate the dynamics of facial expressions or require extensive artistic input while high-resolution performance capture techniques are cost prohibitive. In this thesis, we develop a framework to explore representations of two key facial expressions, blinks and smiles, and we show that data-driven models are needed to realistically animate these expressions. Our approach relies on utilizing high-resolution performance capture data to build models that can be used in traditional keyframing systems. First, we record large collections of high-resolution dynamic expressions through video and motion capture technology. Next, we build expression-specific models of the dynamic data properties of blinks and smiles. We explore variants of the model and assess whether viewers perceive the models as more natural than the simplified models present in the literature. In the first part of the thesis, we build a generative model of the characteristic dynamics of blinks: fast closing of the eyelids followed by a slow opening. Blinks have a characteristic profile with relatively little variation across instances or people. Our results demonstrate the need for an accurate model of eye blink dynamics rather than simple approximations, as viewers perceive the difference. In the second part of the thesis, we investigate how spatial and temporal linearities impact smile genuineness and build a model for genuine smiles. Our perceptual results indicate that a smile model needs to preserve temporal information. With this model, we synthesize perceptually genuine smiles that outperform traditional animation methods accompanied by plausible head motions. In the last part of the thesis, we investigate how blinks synchronize with the start and end of spontaneous smiles. Our analysis shows that eye blinks correlate with the end of the smile and occur before the lip corners stop moving downwards. We argue that the timing of blinks relative to smiles is useful in creating compelling facial expressions. Our work is directly applicable to current methods in animation. For example, we illustrate how our models can be used in the popular framework of blendshape animation to increase realism while keeping the system complexity low. Furthermore, our perceptual results can inform the design of realistic animation systems by highlighting common assumptions that over-simplify the dynamics of expressions.

XML is a widely used data exchange format. The verbose nature of XML leads to the requirement to efficiently store and process this type of data using compression. Various general-purpose transforms and compression techniques exist that can be used to transform and compress XML data. More compact alternatives to XML data have been developed, namely JSON due to the verbosity of XML data. Similarly, there is a requirement to efficiently store and process SMILES data used in Chemoinformatics. General-purpose transforms and compressors can be used to compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. The primary contribution of this research is to provide developers that use XML, JSON or SMILES data, with key knowledge of the best transformation techniques to use with certain types of data, and which compression techniques would provide the best compressed output size and processing times, depending on their requirements. The main study in this thesis, investigates the extent of which using data transforms prior to data compression can further improve the compression of XML and JSON data. It provides a comparative analysis of applying a variety of data transform and data transform variations, to a number of different types of XML and JSON equivalent datasets of various sizes, and applying different general-purpose compression techniques over the transformed data. A case study is also conducted, to investigate data transforms prior to compression to improve the compression of data within a data-specific domain.