IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v19y2017i6d10.1007_s10796-017-9765-z.html
   My bibliography  Save this article

Grammatical facial expression recognition in sign language discourse: a study at the syntax level

Author

Listed:
  • Fernando A. Freitas

    (University of Sao Paulo)

  • Sarajane M. Peres

    (University of Sao Paulo)

  • Clodoaldo A. M. Lima

    (University of Sao Paulo)

  • Felipe V. Barbosa

    (University of Sao Paulo)

Abstract

Facial Expression Recognition is an already well-developed research area, mainly due to its applicability in the construction of different system types. Facial expressions are especially important in the area which relates to the construction of discourses through sign language. Sign languages are visual-spatial languages that are not assisted by voice intonation. Therefore, they use facial expressions to support the manifestation of prosody aspects and some grammatical constructions. Such expressions are called Grammatical Facial Expressions (GFEs) and they are present at sign language morphological and syntactic levels. GFEs stand out in automated recognition processes for sign languages, as they help removing ambiguity among signals, and they also contribute to compose the semantic meaning of discourse. This paper aims to present a study which applies inductive reasoning to recognize patterns, as a way to study the problem involving the automated recognition of GFEs at the discourse syntactic level in the Libras Sign Language (Brazilian Sign Language). In this study, sensor Microsoft Kinect was used to capture three-dimensional points in the faces of subjects who were fluent in sign language, generating a corpus of Libras phrases, which comprised different syntactic constructions. This corpus was analyzed through classifiers that were implemented through neural network Multilayer Perceptron, and then a series of experiments was conducted. The experiments allowed investigating: the recognition complexity that is inherent to each of the GFEs that are present in the corpus; the use suitability of different vector representations, considering descriptive characteristics that are based on coordinates of points in three dimensions, distances and angles therefrom; the need for using time data regarding the execution of expressions during speech; and particularities that are connected to data labeling and the evaluation of classifying models in the context of a sign language.

Suggested Citation

  • Fernando A. Freitas & Sarajane M. Peres & Clodoaldo A. M. Lima & Felipe V. Barbosa, 2017. "Grammatical facial expression recognition in sign language discourse: a study at the syntax level," Information Systems Frontiers, Springer, vol. 19(6), pages 1243-1259, December.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:6:d:10.1007_s10796-017-9765-z
    DOI: 10.1007/s10796-017-9765-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-017-9765-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-017-9765-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shih-Chia Huang & Suzanne McIntosh & Stanislav Sobolevsky & Patrick C. K. Hung, 2017. "Big Data Analytics and Business Intelligence in Industry," Information Systems Frontiers, Springer, vol. 19(6), pages 1229-1232, December.
    2. Liu, Jiamin & Ma, Shuangge & Xu, Wangli & Zhu, Liping, 2022. "A generalized Wilcoxon–Mann–Whitney type test for multivariate data through pairwise distance," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    3. Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:19:y:2017:i:6:d:10.1007_s10796-017-9765-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.