No / If the face is a window to the soul, then the smile is the light that reflects from the soul. The face conveys much information about a person, be it the identity, gender, feelings or even the thought process, e.g. [10, 13–15]. Since the smile is one of the most complex facial expressions, it is of no surprise that it contains much personality traits and other information about the individual.

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.

No / Gender classification has multiple applications including, but not limited to, face perception, age, ethnicity and identity analysis, video surveillance and smart human computer interaction. The majority of computer based gender classification algorithms analyse the appearance of facial features predominantly based on the texture of the static image of the face. In this paper, we propose a novel algorithm for gender classification using the smile dynamics without resorting to the use of any facial texture information. Our experiments suggest that this method has great potential for finding indicators of gender dimorphism. Our approach was tested on two databases, namely the CK+ and the MUG, consisting of a total of 80 subjects. As a result, using the KNN algorithm along with 10-fold cross validation, we achieve an accurate classification rate of 80% for gender simply based on the dynamics of a person's smile.

Yes / In this paper, we report the results of our recent research into the understanding of the exact distribution of a smile across the face, especially the distinction in the weight distribution of a smile between a genuine and a posed smile. To do this, we have developed a computational framework for the analysis of the dynamic motion of various parts of the face during a facial expression, in particular, for the smile expression. The heart of our dynamic smile analysis framework is the use of optical flow intensity variation across the face during a smile. This can be utilised to efficiently map the dynamic motion of individual regions of the face such as the mouth, cheeks and areas around the eyes. Thus, through our computational framework, we infer the exact distribution of weights of the smile across the face. Further, through the utilisation of two publicly available datasets, namely the CK+ dataset with 83 subjects expressing posed smiles and the MUG dataset with 35 subjects expressing genuine smiles, we show there is a far greater activity or weight distribution around the regions of the eyes in the case of a genuine smile. / Supported in part by the European Union's Horizon 2020 Programme H2020-MSCA-RISE-2017, under the project PDE-GIR with grant number 778035.

No / Explains how to implement computational techniques for human smile analysis Shares insights into the human personality traits hidden in a smile Enriches the understanding of human emotions through examples of face analysis Includes key examples of the practical use of computer based smile analysis.

No / How many times have you smiled today? How many times have you frowned today? Ever thought of being in a state of self-consciousness to be able to relate your own mood with your facial emotional expressions? Perhaps with our present-day busy lives, we may not consider these as crucial questions. However, as researchers uncover more and more about the human emotional landscape they are learning the importance of understanding our emotions.

<b>Introduction</b>: Today one of the primary goals of any kind of dental treatment is the achievement of balanced smile aesthetics, as patients increasingly attend dental clinics to improve their appearance. The main aim of the present study was to assess and compare the smile aesthetics created by the use of two orthodontic bracket systems (self-ligating vs. conventional ligating) as a part of analysing secondary outcomes of two randomised clinical trials comparing between these two systems. <b>Methodology</b>: The assessment of smile aesthetics was done by analysing and scoring post-orthodontic treatment 125 frontal smile photographs subjectively and objectively. The subjective evaluation was performed by 20 dental professionals and 20 laypeople, while the objective assessment was done by one principal examiner using a group of smile aesthetics parameters. <b>Statistical analysis</b>: Multiple regression statistical analyses were performed to test the association between subjective and objective assessment of smile aesthetics in order to find the significant smile aesthetics predictors and assess the effect of the bracket type (self-ligating vs conventional) on the resulting smile aesthetics. <b>Results</b>: The finding from this research shows that the bracket type was not an important smile aesthetics factor in all the statistical models, although there are other important smile aesthetics factors as there was a significant correlation between the subjective and objective assessment of smile aesthetics parameters (Pearson’s correlation coefficients “r” > 0.50). <b>Conclusion</b>: There is insufficient evidence to reject the null hypotheses of no significant difference in the smile aesthetics created by the two orthodontic bracket systems. An Orthodontic Smile Aesthetics Rating (OSAR) tool has been developed.