Multi-task Facial Landmark Detection Network for Early ASD Screening

被引：0

作者：

Lin, Ruihan ^{[1
]}

Zhang, Hanlin ^{[1
]}

Wang, Xinming ^{[1
]}

Ren, Weihong ^{[1
]}

Wu, Wenhao ^{[1
]}

Liu, Zuode ^{[1
]}

Xu, Xiu ^{[2
]}

Xu, Qiong ^{[2
]}

Liu, Honghai ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Mech Engn & Automat, Shenzhen, Peoples R China

[2] Fudan Univ, Childrens Hosp, Dept Child Hlth Care, Shanghai, Peoples R China

来源：

INTELLIGENT ROBOTICS AND APPLICATIONS (ICIRA 2022), PT I | 2022年 / 13455卷

关键词：

Autism; Joint attention; Multi-task facial landmark detection; AUTISM; CHILDREN;

D O I：

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

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：381 / 391

页数：11

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