Vision-Based Approach for Autism Diagnosis Using Eye Tracking based on Machine Learning and Deep Learning

Ali F. Rashid, Shaimaa H. Shaker


Although the early diagnosis of autism spectrum disorder (ASD) is highly sought, it is still a challenging task that requires a battery of cognitive tests and hours of clinical exams to be properly accomplished. ASD may manifest itself in various ways, which makes diagnosing it even more challenging. However, although diagnostic tests are generally devised by specialists, human mistakes are still possible. Computer-assisted technologies can be quite useful in this respect for aiding in the selection procedure. The use of eye tracking as a crucial component of ASD screening evaluation based on the distinctive characteristics of the eye gaze is continued in this article. This research adds to the growing body of evidence that eye tracking technologies may help in ASD screening. Eye tracking, visualization, using machine learning and deep learning are part of the suggested solution. Eye-tracking scan paths were first converted into a visual representation in the form of a series of pictures. After that, an image classification job was taught to a convolutional neural network. The findings of the experiments showed that using a visual representation simplified the diagnostic process while also achieving excellent accuracy. The convolutional neural network model, in particular, can attain promising classification accuracy. This shows that visuals are capable of encoding information about gaze movements and its underlying dynamics. Based on the greatest information coefficient, we examined potential relationships between autism severity and eye movement dynamics. The results demonstrate that using the great potential in combining eye tracking, visualization, machine learning, and deep learning to create an impartial tool to help in ASD screening. Generally, the approach we propose may be used to identify various diseases, including neurodevelopmental problems.


Keywords: autism spectrum disorders, eye tracking, deep learning, machine learning.

Full Text:



GRIFFITHS S., JARROLD C., PENTON-VOAK I. S., WOODS A. T., SKINNER A. L., and MUNAFÒ M. R. Impaired Recognition of Basic Emotions from Facial Expressions in Young People with Autism Spectrum Disorder: Assessing the Importance of Expression Intensity. Journal of Autism and Developmental Disorders, 2019, 49(7): 2768–2778.

MANFREDONIA J., BANGERTER A., MANYAKOV N. V., NESS S., LEWIN D., SKALKIN A., BOICE M., GOODWIN M. S., DAWSON G., HENDREN R., LEVENTHAL B., SHIC F., and PANDINA G. Automatic Recognition of Posed Facial Expression of Emotion in Individuals with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 2019, 49(1): 279–293.

BANIRE B., AL-THANI D., MAKKI M., QARAQE M. K., ANAND K., CONNOR O. B., KHOWAJA K., and MANSOOR B. Attention Assessment: Evaluation of Facial Expressions of Children with Autism Spectrum Disorder. In: ANTONA M., & STEPHANIDIS C. (eds.) Universal Access in Human-Computer Interaction. Multimodality and Assistive Environments. HCII 2019. Lecture Notes in Computer Science(), Vol. 11573. Springer, Cham, 2019: 32–48.

LI B., MEHTA S., ANEJA D., FOSTER C., VENTOLA P., SHIC F., and SHAPIRO L. A Facial Affect Analysis System for Autism Spectrum Disorder. Proceedings of the IEEE International Conference on Image Processing, Taipei, 2019, pp. 4549–4553.

SENJU A., & JOHNSON M. H. Is eye contact the key to the social brain? Behavioral and Brain Sciences, 2010, 33: 458-459.

OMA K. S., MONDAL P., KHAN N. S., RIZVI M. R. K., and ISLAM M. N. A Machine Learning Approach to Predict Autism Spectrum Disorder. Proceedings of the International Conference on Electrical, Computer and Communication Engineering, Cox'sBazar, 2019, pp. 7–9.

CARETTE R., ELBATTAH M., DEQUEN G., GUÉRIN J. L., and CILIA F. Visualization of eye-tracking patterns in autism spectrum disorder: method and dataset. Proceedings of the Thirteenth International Conference on Digital Information Management, Berlin, 2018, pp. 248-253.

COONROD E. E., & STONE W. L. Early concerns of parents of children with autistic and nonautistic disorders. Infants and Young Children, 2004, 17(3): 258–268.

PUSIOL G., ESTEVA A., HALL S. S., FRANK M., MILSTEIN A., and FEI-FEI L. Vision-based classification of developmental disorders using eye-movements. In: OURSELIN S., JOSKOWICZ L., SABUNCU M., UNAL G., and WELLS W. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science(), Vol. 9901. Springer, Cham, 2016: 317-325.

VABALAS A., & FREETH M. Brief Report: Patterns of Eye Movements in Face to Face Conversation are Associated with Autistic Traits: Evidence from a Student Sample. Journal of Autism and Developmental Disorders, 2016, 46: 305–314.

PIERCE K., CONANT D., HAZIN R., STONER R., and DESMOND J. Preference for geometric patterns early in life as a risk factor for autism. Archives of General Psychiatry, 2011, 68(1): 101–109.

JONES W., & KLIN A. Attention to eyes is present but in decline in 2-6-month-old infants later diagnosed with autism. Nature, 2013, 504(7480): 427-431.

FRAZIER T. W., KLINGEMIER E. W., BEUKEMANN M., SPEER L., MARKOWITZ L., PARIKH S., WEXBERG S., GIULIANO K., SCHULTE E., DELAHUNTY C., AHUJA V., ENG C., MANOS M. J., HARDAN A. Y., YOUNGSTROM E. A., and STRAUSS M. S. Development of an objective autism risk index using remote eye tracking. Journal of the American Academy of Child & Adolescent Psychiatry, 2016, 55(4): 301-309.

BRADSKI G., & KAEHLER A. Histograms and Matching. In: Learning OpenCV. O’Reilly Media, Sebastopol, California, 2008: 193-221.

XU Y., JIA R., MOU L., LI G., CHEN Y., LU Y., and JIN Z. Improved relation classification by deep recurrent neural networks with data augmentation. Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, 2016, pp. 1461–1470.

WASKOM M. L. Seaborn: statistical data visualization. Journal of Open Source Software, 2021, 6(60): 3021.

RAMMO F. M., & AL-HAMDANI M. N. Detecting The Speaker Language Using CNN Deep Learning Algorithm. Iraqi Journal for Computer Science and Mathematics, 2022, 3(1): 43–52.

MAHDIZADEHAGHDAM S., PANAHI A., and KRIM H. Sparse generative adversarial network. Proceedings of the International Conference on Computer Vision Workshop, Seoul, 2019, pp. 3063–3071.


  • There are currently no refbacks.