Skip to main content
SpringerLink
Log in

A Review on Facial Expression Based Behavioral Analysis Using Computational Technique for Autistic Disorder Patients

  • Conference paper
  • First Online:
Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1046))

Included in the following conference series:

Abstract

Within recent decades the chances of a child being diagnosed with autism spectrum disorder have increased dramatically. Individuals with autism disorder have markedly different social and emotional actions and reactions than non-autistic individuals. It is a chronic disorder whose symptoms include failure to develop normal social relations with other people, impaired development of communicative ability, lack of imaginative ability, and repetitive, stereotyped movements. There exist numerous techniques associated to detect autism disorders in children. Facial expression-based method is an effective technique frequently used by medical experts to detect the emotional patterns of autistic children. Our paper reviews this technique to determine the behavioral analysis of autistic children. Comparative analysis of existing techniques is undertaken to select the most optimal technique of autism detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li, Y., Elmaghraby, A.S.: A Framework for using games for behavioral analysis of autistic children. In: Computer Games: AI, Animation, Mobile, Multimedia, Educational and Serious Games (CGAMES), pp. 1–4. IEEE, Louisville (2014)

    Google Scholar 

  2. Abirached, B., et al.: Improving communication skills of children with ASDs through interaction with visual characters. In: 1st International Conference on Serious Games and Applications for Health (SeGAH), pp. 1–4. IEEE, Braga (2011)

    Google Scholar 

  3. Heni, N., Hamam, H.: Design of emotional educational system mobile games for autistic children. In: 2nd International Conference on Advanced Technologies for Signal and Image Processing, pp. 631–637. IEEE, Monastir (2016)

    Google Scholar 

  4. Lacroix, A., Guidette, M., Roge, B., Reilly, J.: Facial emotion recognition in 4-to 8-year-olds with autism spectrum disorder: A developmental trajectory approach. Res. Autism Spectr. Disord. 8(9), 1146–1154 (2014)

    Article  Google Scholar 

  5. Chen, C.H., Lee, I.J., Lin, L.Y.: Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. Res. Dev. Disabil. 36(8), 396–403 (2014)

    Google Scholar 

  6. Cheng, Y., Luo, S.Y., Lin, H.C.: Investigating the performance on comprehending 3D social emotion through a mobile learning system for individuals with autistic spectrum disorder. In: 5th IIAI International Congress on Advanced Applied Informatics, pp. 414–417. IEEE, Kumamoto (2016)

    Google Scholar 

  7. Del Coco, M., et al.: A computer vision based approach for understanding emotional involvements in children with autism spectrum disorders. In: International Conference on Computer Vision Workshops, pp. 1401–1407. IEEE, Venice (2017)

    Google Scholar 

  8. Jazouli, M., Majda, A., Zarghili, A.: A $P recognizer for automatic facial emotion recognition using Kinect sensor. In: Intelligent Systems and Computer Vision (ISCV), pp. 402–406. IEEE, Fez (2017)

    Google Scholar 

  9. Silva, V., Soares, F., Esteves, J.S.: Mirroring and recognizing emotions through facial expressions for a RoboKind platform. In: 5th Portuguese Meeting on Bioengineering (ENBENG), pp. 1–4. IEEE, Coimbra (2017)

    Google Scholar 

  10. Griffiths, S., Jarrold, C., Penton-Voak, I.S., Woods, A.T., Skinner, A.L., Munafò, M.R.: Impaired recognition of basic emotions from facial expressions in young people with autism spectrum disorder. J. Autism Dev. Disord. 47, 1–11 (2017)

    Article  Google Scholar 

  11. Trigeorgis, G., et al.: Adieu features?: End-to-end speech emotion recognition using a deep convolutional recurrent network. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5200–5204. IEEE, Sanghai (2016)

    Google Scholar 

  12. Gong, Y., Poellabauer, C.: Continuous assessment of children’s emotional states using acoustic analysis. In: International Conference on Healthcare Informatics (ICHI), pp. 5200–5204. IEEE, Park City Park City (2017)

    Google Scholar 

  13. Tzirakis, P., Trigeorgis, G., Nicolaou, M.A., Schuller, B.W., Zafeiriou, S.: End-to-end multimodal emotion recognition using deep neural networks. IEEE J. Sel. Top. Sig. Process. 11(6), 1301–1309 (2017)

    Article  Google Scholar 

  14. Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10. IEEE, Lake Placid (2016)

    Google Scholar 

  15. Yeung, M.K., Han, Y.M.Y., Sze, S.L., Chan, A.S.: Altered right frontal cortical connectivity during facial emotion recognition in children with autism spectrum disorders. Res. Autism Spect. Disord. 8(11), 1567–1577 (2014)

    Article  Google Scholar 

  16. Rodriguez-Bermudez, G., Garcia-Laencina, P.: Analysis of EEG signals using nonlinear dynamics and chaos: a review. Appl. Math. Inf. Sci. Int. J. Nat. Sci. 9(5), 2309–2321 (2015)

    MathSciNet  Google Scholar 

  17. Othmana, M., Wahaba, A., Karima, I., Dzulkifli, M.A., Alshaiklia, I.F.T.: EEG emotion recognition based on the dimensional models of emotions. Proc. Soc. Behav. Sci. 97, 30–37 (2013)

    Article  Google Scholar 

  18. Black, M.H., et al.: Mechanisms of facial emotion recognition in autism spectrum disorders: insights from eye tracking and electroencephalography. Neurosci. Biobehav. Rev. 80, 488–515 (2017)

    Article  Google Scholar 

  19. Jamil, N., Khir, N.H.M., Ismail, M., Razak, F.H.A.: Gait-based emotion detection of children with autism spectrum disorders: a preliminary investigation. Proc. Comput. Sci. 76, 342–348 (2015)

    Article  Google Scholar 

  20. Zhou, Y., Yu, F., Duong, T.: Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PlosOne 9(6), 1–14 (2014)

    Google Scholar 

  21. Leo, M., Del Coco, M., Carcagn, P., Distante, C.: Automatic emotion recognition in robot-children interaction for ASD Treatment. In: IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 537–545. IEEE, Santiago (2015)

    Google Scholar 

  22. Swangnetr, M., Kaber, D.B.: Emotional state classification in patient–robot interaction using wavelet analysis and statistics-based feature selection. IEEE Trans. Hum.-Mach. Syst. 43(1), 63–75 (2013)

    Article  Google Scholar 

  23. Soares, F., et al.: Robotica-Autismo project: technology for autistic children. In: 3rd Portuguese Meeting in Bioengineering IEEE (ENBENG), pp. 1–4. IEEE, Braga (2013)

    Google Scholar 

  24. Fan, J., Bekele, E., Warren, Z., Sarkar, N.: EEG analysis of facial affect recognition process of individuals with ASD performance prediction leveraging social context. In: Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 38–43. IEEE, San Antonio (2017)

    Google Scholar 

  25. Li, B.Y., Mian, A.S., Liu, W., Krishna, A.: Using Kinect for face recognition under varying poses, expressions, illumination and disguise. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 186–192. IEEE, Tampa (2013)

    Google Scholar 

  26. Manfredonia, J., et al.: Automatic recognition of posed facial expression of emotion in individuals with autism spectrum disorder. J. Autism Dev. Disord. 49(1), 279–293 (2019)

    Article  Google Scholar 

  27. Golan, O., Gordon, I., Fichman, K., Keinan, G.: Specific patterns of emotion recognition from faces in children with ASD: results of a cross-modal matching paradigm. J. Autism Dev. Disord. 48(3), 844–852 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

The paper is one of the review paper of behavioral analysis of the autistic child before starting my implementation to diagnose the autistic child. I want to express my most profound thankfulness to each one of the individuals who has given me the likelihood to complete the paper. An exceptional appreciation I provide for my guide and my co-guide in invigorating recommendations and support, helped me to organize my point particularly in composing this paper. I would like to express my gratitude to the dean of my college for being a greater part to complete my paper.

Author information

Authors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, 751024, Odisha, India

    Camellia Ray, Hrudaya Kumar Tripathy & Sushruta Mishra

Authors
  1. Camellia Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hrudaya Kumar Tripathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sushruta Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Camellia Ray .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ray, C., Tripathy, H.K., Mishra, S. (2019). A Review on Facial Expression Based Behavioral Analysis Using Computational Technique for Autistic Disorder Patients. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_43

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9942-8_43

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9941-1

  • Online ISBN: 978-981-13-9942-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation