Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May 31;11(6):734.
doi: 10.3390/brainsci11060734.

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

Tania Akter  1   2 , Mohammad Hanif Ali  1 , Md Imran Khan  2 , Md Shahriare Satu  3 , Md Jamal Uddin  4 , Salem A Alyami  5 , Sarwar Ali  6 , Akm Azad  7 , Mohammad Ali Moni  8   9
Affiliations

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

Tania Akter et al. Brain Sci. .

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
A standard convolution (left panel) and a depth-wise separable convolution (right panel).
Figure 1
Figure 1
The schematic diagram of our proposed transfer-learning-based facial recognition framework. (A) Data pre-processing step: organization of raw images for further activities; (B) Evaluation of Baseline Classifiers: performance analysis of improved models with state-of-the-art classifiers; (C) Identification of autism clusters: investigate individual clustering groups and explore the best group using machine learning model.
Figure 2
Figure 2
Improved MobileNet-V1 Transfer Learning Model.
Figure 3
Figure 3
Comparison of ROC curves obtained for (a) validation set (b) test set using improved MobileNet-V1 along with other classifiers.
Figure 4
Figure 4
Accuracy of improved MobileNet-V1 for different clustered group, where the x-axis labels indicate different values of K = 2, 3, ..., 10, and the y-axis label shows the accuracy.
See this image and copyright information in PMC

References

    1. Goh K.L., Morris S., Rosalie S., Foster C., Falkmer T., Tan T. Typically developed adults and adults with autism spectrum disorder classification using centre of pressure measurements; Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Shanghai, China. 20–25 March 2016; pp. 844–848. ISSN 2379-190X. - DOI
    1. Speaks A. What is autism. Retrieved Novemb. 2011;17:2011.
    1. Satu M.S., Farida Sathi F., Arifen M.S., Hanif Ali M., Moni M.A. Early Detection of Autism by Extracting Features: A Case Study in Bangladesh; Proceedings of the 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST); Dhaka, Bangladesh. 10–12 January 2019; pp. 400–405. - DOI
    1. Guillon Q., Hadjikhani N., Baduel S., Rogé B. Visual social attention in autism spectrum disorder: Insights from eye tracking studies. Neurosci. Biobehav. Rev. 2014;42:279–297. doi: 10.1016/j.neubiorev.2014.03.013. - DOI - PubMed
    1. Haque M.I.U., Valles D. A Facial Expression Recognition Approach Using DCNN for Autistic Children to Identify Emotions; Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON); Vancouver, BC, Canada. 1–3 November 2018; pp. 546–551. - DOI