Abstract
Joint attention is an important skill that involves coordinating the attention of at least two individuals towards an object or event in early child development, which is usually absent in children with autism. Children’s joint attention is an essential part of the diagnosis of autistic children. To improve the effectiveness of autism screening, in this paper, we propose a multi-task facial landmark detection network to enhance the stability of gaze estimation and the accuracy of the joint attention screening result. In order to verify the proposed method, we recruit 39 toddlers aged from 16 to 32 months in this study and build a children-based facial landmarks dataset from 19 subjects. Experiments show that the accuracy of the joint attention screening result is 92.5\(\%\), which demonstrates the effectiveness of our method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cheng, Y., Wang, H., Bao, Y., Lu, F.: Appearance-based gaze estimation with deep learning: a review and benchmark. arXiv preprint arXiv:2104.12668 (2021)
Dapogny, A., Bailly, K., Cord, M.: DeCaFA: deep convolutional cascade for face alignment in the wild. In: IEEE International Conference on Computer Vision, pp. 6893–6901 (2019)
Deng, J., Guo, J., Ververas, E., Kotsia, I., Zafeiriou, S.: RetinaFace: single-shot multi-level face localisation in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5203–5212 (2020)
Dong, X., Yang, Y.: Teacher supervises students how to learn from partially labeled images for facial landmark detection. In: IEEE International Conference on Computer Vision, pp. 783–792 (2019)
Hazlett, H.C., et al.: Early brain development in infants at high risk for autism spectrum disorder. Nature 7641, 348–351 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jin, H., Liao, S., Shao, L.: Pixel-in-pixel net: towards efficient facial landmark detection in the wild. Int. J. Comput. Vision 12, 3174–3194 (2021)
Kaliouby, R.E., Picard, R., Baron-Cohen, S.: Affective computing and autism. Ann. N. Y. Acad. Sci. (1), 228–248 (2006)
Li, J., et al.: Appearance-based gaze estimation for ASD diagnosis. IEEE Trans. Cybern. (2022)
Liu, J., et al.: Early screening of autism in toddlers via response-to-instructions protocol. IEEE Trans. Cybern. (2020)
Magiati, I., Charman, T., Howlin, P.: A two-year prospective follow-up study of community-based early intensive behavioural intervention and specialist nursery provision for children with autism spectrum disorders. J. Child Psychol. Psychiatry 8, 803–812 (2007)
Matsentidou, S., Poullis, C.: Immersive visualizations in a VR cave environment for the training and enhancement of social skills for children with autism. In: International Conference on Computer Vision Theory and Applications (VISAPP), pp. 230–236. IEEE (2014)
Ming, S., Mulhern, T., Stewart, I., Moran, L., Bynum, K.: Training class inclusion responding in typically developing children and individuals with autism. J. Appl. Behav. Anal. 1, 53–60 (2018)
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: The first facial landmark localization challenge. In: IEEE international conference on computer vision workshops. pp. 397–403 (2013)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483 (2013)
Trigeorgis, G., Snape, P., Nicolaou, M.A., Antonakos, E., Zafeiriou, S.: Mnemonic descent method: a recurrent process applied for end-to-end face alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4177–4187 (2016)
Wang, X., Zhang, J., Zhang, H., Zhao, S., Liu, H.: Vision-based gaze estimation: a review. IEEE Trans. Cogn. Dev. Syst. (2021)
Wang, Z., Liu, J., He, K., Xu, Q., Xu, X., Liu, H.: Screening early children with autism spectrum disorder via response-to-name protocol. IEEE Trans. Industr. Inf. 1, 587–595 (2019)
Wang, Z., Liu, J., Zhang, W., Nie, W., Liu, H.: Diagnosis and intervention for children with autism spectrum disorder: a survey. IEEE Trans. Cogn. Dev. Syst. (2021)
Zhang, H., et al.: Gaze-driven interaction system for cognitive ability assessment. In: International Conference on Intelligent Control and Information Processing (ICICIP), pp. 346–351. IEEE (2021)
Zhang, W., Wang, Z., Liu, H.: Vision-based joint attention detection for autism spectrum disorders. In: Sun, F., Liu, H., Hu, D. (eds.) ICCSIP 2018. CCIS, vol. 1005, pp. 26–36. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-7983-3_3
Zhou, Y., Gregson, J.: WHENet: real-time fine-grained estimation for wide range head pose. arXiv preprint arXiv:2005.10353 (2020)
Zhu, S., Li, C., Change Loy, C., Tang, X.: Face alignment by coarse-to-fine shape searching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4998–5006 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, R. et al. (2022). Multi-task Facial Landmark Detection Network for Early ASD Screening. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13455. Springer, Cham. https://doi.org/10.1007/978-3-031-13844-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-13844-7_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13843-0
Online ISBN: 978-3-031-13844-7
eBook Packages: Computer ScienceComputer Science (R0)