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Analysis of institutional authors

Mazher MAuthor

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Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children

Publicated to:Image And Vision Computing. 119 104375- - 2022-03-01 119(), DOI: 10.1016/j.imavis.2022.104375

Authors: Qayyum, Abdul; Razzak, Imran; Moustafa, Nour; Mazher, Moona

Affiliations

CNRS, UMR 6285, LabSTICC, ENIB, Brest, France - Author
Deakin Univ, Sch Informat Technol, Geelong, Vic, Australia - Author
Deakin University - Author
Dijon Univ, Dept Elect & Comp Engn, Dijon, France - Author
Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance (Lab-Sticc) , Université de Bourgogne - Author
Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT, Australia - Author
Univ Rovira & Virgili, Dept Comp Engn & Math, Tarragona, Spain - Author
Universitat Rovira i Virgili - Author
University of New South Wales at Australian Defence Force Academy - Author
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Abstract

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively.

Keywords

depressedemotion carefacial behavior recognitionhuman computer interactionimpactsliteracy interventionpatient monitoringprogressive shallownetDepressedEmotion careExpression recognitionFacial behavior recognitionHuman computer interactionPatient monitoringProgressive shallownetPsychological health

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Image And Vision Computing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 20/108, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Software Engineering.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 3.07, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-29, the following number of citations:

  • WoS: 6
  • Scopus: 9

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-29:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 66.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 66 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Australia; France.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Mazher, Moona).