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Automated vs Human Recognition of Emotional Facial Expressions of High-Functioning Children with Autism in a Diagnostic-Technological Context: Explorations via a Bottom-Up Approach

  • Miklos Gyori
  • Zsófia Borsos
  • Krisztina Stefanik
  • Zoltán Jakab
  • Fanni Varga
  • Judit Csákvári
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10896)

Abstract

Early detection of autism spectrum conditions (ASC) is an important goal. Automated facial expression recognition is a promising approach and has implications for assistive and educational technologies, too. This study was an initial exploration of (1) the inter-rater reliability of human recognition of facial emotions of high functioning (HF) children with ASC; (2) the relationship between human and automated recognition of facial emotions; and (3) a ‘bottom-up’ approach on identifying ASC/typical development (TD) differences, from a screening serious game context. Thirteen HF, kindergarten-age children with ASC and 13 children with TD, matched along age and IQ, participated. Emotion recognition was administered on video-recordings from sessions of their playing with the serious game. Results showed lack of inter-rater reliability in human coding, confirming some advantages of machine coding. The simple bottom-up cross-sectional exploratory analysis did not reveal any ASC/TD difference. This is in contrast with our and others’ previous results, indicating such differences when aggregating emotion data from wider time-windows in machine-coded data-sets. This suggests that this second approach may be a more promising one to identify autism-specific emotion expression patterns.

Keywords

Autism spectrum conditions Emotional facial expressions Screening Serious game 

Notes

Ethical Approval and Acknowledgements

This research was approved by the Research Ethics Committee of the ‘Barczi Gusztav’ Faculty of Special Education, ELTE University, Budapest, Hungary. Some elements were funded by a grant within the EIT ICT Labs Hungarian Node (PI: András Lőrincz), and via a TÁMOP grant, co-financed by the European Union and the government of Hungary (TÁMOP 4.2.1./B-09/KMR-2010-0003). The work of M Gyori, Zs Borsos and K Stefanik was supported by a grant from the Hungarian Academy of Sciences within its Content Pedagogy Programme. Authors wish to thank András Lőrincz and Tibor Gregorics for their contributions and support in earlier phases of the project.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ELTE UniversityBudapestHungary
  2. 2.MTA-ELTE ‘Autism in Education’ Research GroupBudapestHungary

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