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    A genuine smile is indeed in the eyes – The computer aided non-invasive analysis of the exact weight distribution of human smiles across the face

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    ugail_and_al-dahoud_2019 (1.640Mb)
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    Publication date
    2019-10
    Author
    Ugail, Hassan
    Al-dahoud, Ahmad
    Keyword
    Genuine smiles
    Posed smiles
    Smile analysis
    Smile weight distribution
    Rights
    © 2019 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
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    Abstract
    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.
    URI
    http://hdl.handle.net/10454/18938
    Version
    Accepted manuscript
    Citation
    Ugail H and Al-dahoud A (2019) A genuine smile is indeed in the eyes - The computer aided non-invasive analysis of the exact weight distribution of human smiles across the face. Advanced Engineering Informatics. 42: 100967.
    Link to publisher’s version
    https://doi.org/10.1016/j.aei.2019.100967
    Type
    Article
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    Engineering and Informatics Publications

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      Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition

      Ugail, Hassan; Al-dahoud, Ahmad (2018-09)
      Automatic gender classification has become a topic of great interest to the visual computing research community in recent times. This is due to the fact that computer-based automatic gender recognition has multiple applications including, but not limited to, face perception, age, ethnicity, identity analysis, video surveillance and smart human computer interaction. In this paper, we discuss a machine learning approach for efficient identification of gender purely from the dynamics of a person’s smile. Thus, we show that the complex dynamics of a smile on someone’s face bear much relation to the person’s gender. To do this, we first formulate a computational framework that captures the dynamic characteristics of a smile. Our dynamic framework measures changes in the face during a smile using a set of spatial features on the overall face, the area of the mouth, the geometric flow around prominent parts of the face and a set of intrinsic features based on the dynamic geometry of the face. This enables us to extract 210 distinct dynamic smile parameters which form as the contributing features for machine learning. For machine classification, we have utilised both the Support Vector Machine and the k-Nearest Neighbour algorithms. To verify the accuracy of our approach, we have tested our algorithms on two databases, namely the CK+ and the MUG, consisting of a total of 109 subjects. As a result, using the k-NN algorithm, along with tenfold cross validation, for example, we achieve an accurate gender classification rate of over 85%. Hence, through the methodology we present here, we establish proof of the existence of strong indicators of gender dimorphism, purely in the dynamics of a person’s smile.
    • Thumbnail

      Computational analysis of smile weight distribution across the face for accurate distinction between genuine and posed smiles

      Al-dahoud, Ahmad; Ugail, Hassan (2018)
      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.
    • Thumbnail

      A Comparative Study of Data Transformations for Efficient XML and JSON Data Compression. An In-Depth Analysis of Data Transformation Techniques, including Tag and Capital Conversions, Character and Word N-Gram Transformations, and Domain-Specific Data Transforms using SMILES Data as a Case Study

      Ridley, Mick J.; Cullen, Andrea J.; Scanlon, Shagufta A. (University of BradfordSchool of Electrical Engineering and Computer Science, 2017-12-13)
      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.
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