Multi-task Facial Landmark Detection Network for Early ASD Screening

Ruihan Lin; Hanlin Zhang; Xinming Wang; Weihong Ren; Wenhao Wu; Zuode Liu; Xiu Xu; Qiong Xu; Honghai Liu

doi:10.1007/978-3-031-13844-7_37

Multi-task Facial Landmark Detection Network for Early ASD Screening

Ruihan Lin
, Hanlin Zhang
, Xinming Wang
, Weihong Ren
, Wenhao Wu
, Zuode Liu
, Xiu Xu
, Qiong Xu
, Honghai Liu^*

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Children's Hospital of Fudan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings
Editors	Honghai Liu, Weihong Ren, Zhouping Yin, Lianqing Liu, Li Jiang, Guoying Gu, Xinyu Wu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	381-391
Number of pages	11
ISBN (Print)	9783031138430
DOIs	https://doi.org/10.1007/978-3-031-13844-7_37
State	Published - 2022
Externally published	Yes
Event	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 - Harbin, China Duration: 1 Aug 2022 → 3 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13455 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022
Country/Territory	China
City	Harbin
Period	1/08/22 → 3/08/22

Keywords

Autism
Joint attention
Multi-task facial landmark detection

Access to Document

10.1007/978-3-031-13844-7_37

Cite this

Lin, R., Zhang, H., Wang, X., Ren, W., Wu, W., Liu, Z., Xu, X., Xu, Q., & Liu, H. (2022). Multi-task Facial Landmark Detection Network for Early ASD Screening. In H. Liu, W. Ren, Z. Yin, L. Liu, L. Jiang, G. Gu, & X. Wu (Eds.), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings (pp. 381-391). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13455 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-13844-7_37

Lin, Ruihan ; Zhang, Hanlin ; Wang, Xinming et al. / Multi-task Facial Landmark Detection Network for Early ASD Screening. Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. editor / Honghai Liu ; Weihong Ren ; Zhouping Yin ; Lianqing Liu ; Li Jiang ; Guoying Gu ; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 381-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1d4751b23e5c44a68d8554baffca2802,

title = "Multi-task Facial Landmark Detection Network for Early ASD Screening",

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{\textquoteright}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.",

keywords = "Autism, Joint attention, Multi-task facial landmark detection",

author = "Ruihan Lin and Hanlin Zhang and Xinming Wang and Weihong Ren and Wenhao Wu and Zuode Liu and Xiu Xu and Qiong Xu and Honghai Liu",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 ; Conference date: 01-08-2022 Through 03-08-2022",

year = "2022",

doi = "10.1007/978-3-031-13844-7\_37",

language = "英语",

isbn = "9783031138430",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "381--391",

editor = "Honghai Liu and Weihong Ren and Zhouping Yin and Lianqing Liu and Li Jiang and Guoying Gu and Xinyu Wu",

booktitle = "Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings",

address = "德国",

}

Lin, R, Zhang, H, Wang, X, Ren, W, Wu, W, Liu, Z, Xu, X, Xu, Q & Liu, H 2022, Multi-task Facial Landmark Detection Network for Early ASD Screening. in H Liu, W Ren, Z Yin, L Liu, L Jiang, G Gu & X Wu (eds), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13455 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 381-391, 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, Harbin, China, 1/08/22. https://doi.org/10.1007/978-3-031-13844-7_37

Multi-task Facial Landmark Detection Network for Early ASD Screening. / Lin, Ruihan; Zhang, Hanlin; Wang, Xinming et al.
Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. ed. / Honghai Liu; Weihong Ren; Zhouping Yin; Lianqing Liu; Li Jiang; Guoying Gu; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. p. 381-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13455 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-task Facial Landmark Detection Network for Early ASD Screening

AU - Lin, Ruihan

AU - Zhang, Hanlin

AU - Wang, Xinming

AU - Ren, Weihong

AU - Wu, Wenhao

AU - Liu, Zuode

AU - Xu, Xiu

AU - Xu, Qiong

AU - Liu, Honghai

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Autism

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KW - Multi-task facial landmark detection

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DO - 10.1007/978-3-031-13844-7_37

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AN - SCOPUS:85135854698

SN - 9783031138430

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 391

BT - Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings

A2 - Liu, Honghai

A2 - Ren, Weihong

A2 - Yin, Zhouping

A2 - Liu, Lianqing

A2 - Jiang, Li

A2 - Gu, Guoying

A2 - Wu, Xinyu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022

Y2 - 1 August 2022 through 3 August 2022

ER -

Lin R, Zhang H, Wang X, Ren W, Wu W, Liu Z et al. Multi-task Facial Landmark Detection Network for Early ASD Screening. In Liu H, Ren W, Yin Z, Liu L, Jiang L, Gu G, Wu X, editors, Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 381-391. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-13844-7_37