Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage

Tania Akter; Mohammad Hanif Ali; Md Imran Khan; Md Shahriare Satu; Md Jamal Uddin; Salem A Alyami; Sarwar Ali; Akm Azad; Mohammad Ali Moni

doi:10.3390/brainsci11060734

Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage

Brain Sci. 2021 May 31;11(6):734. doi: 10.3390/brainsci11060734.

Authors

Tania Akter^{1

2}, Mohammad Hanif Ali¹, Md Imran Khan², Md Shahriare Satu³, Md Jamal Uddin⁴, Salem A Alyami⁵, Sarwar Ali⁶, Akm Azad⁷, Mohammad Ali Moni^{8

9}

Affiliations

¹ Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh.
² Department of Computer Science and Engineering, Gono Bishwabidyalay, Savar, Dhaka 1344, Bangladesh.
³ Department of Management Information Systems, Noakhali Science and Technology University, Sonapur, Noakhali 3814, Bangladesh.
⁴ Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj Town Road, Gopalgonj 8100, Bangladesh.
⁵ Department of Mathematics and Statistics, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia.
⁶ Department of Electrical and Electronics Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh.
⁷ School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
⁸ WHO Collaborating Centre on eHealth, UNSW Digital Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia.
⁹ Healthy Aging Theme, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.

Abstract

Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.

Keywords: MobileNet-V1; autism; classifier; clustering; facial images; transfer learning.