Prediction of Autism Spectrum Disorder in Children using Face Recognition

Arumugam, Sajeev Ram; Balakrishna, R.; Khilar, Rashmita; Manoj, Oswalt; Shylaja, C. S.

doi:10.1109/icosec51865.2021.9591679

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…Arumugam et al [66] trained a new CNN architecture with the Kaggle dataset of potentially healthy children or with autism, achieving 91% accuracy (estimated using one holdout).…”

Section: Resultsmentioning

confidence: 99%

Computer aided diagnosis of neurodevelopmental disorders and genetic syndromes based on facial images – A systematic literature review

Rosindo Daher de Barros,

Novais F. da Silva,

de Castro Michelassi

et al. 2023

Heliyon

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“…Arumugam et al [66] trained a new CNN architecture with the Kaggle dataset of potentially healthy children or with autism, achieving 91% accuracy (estimated using one holdout).…”

Section: Resultsmentioning

confidence: 99%

Computer aided diagnosis of neurodevelopmental disorders and genetic syndromes based on facial images – A systematic literature review

Rosindo Daher de Barros,

Novais F. da Silva,

de Castro Michelassi

et al. 2023

Heliyon

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“…Face landmarks and CNNs were used for categorization and emotion detection, obtaining good accuracy across datasets. The study in [27] used a dataset that was made accessible to the public on the Kaggle platform, dividing training and testing into a 70:30 ratio. In the end, the neural network-based model that was constructed had a 91% accuracy rate and a loss value of 0.53.…”

Section: Introductionmentioning

confidence: 99%

Emotion-based Autism Spectrum Disorder Detection by Leveraging Transfer Learning and Machine Learning Algorithms

Sarwani,

Bhaskari,

Bhamidipati

2024

ijacsa

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Autism Spectrum Disorder (ASD) presents as a neurodevelopmental condition impacting social interaction, communication, and behavior, underscoring the imperative of early detection and intervention to enhance outcomes. This paper introduces a novel approach to ASD detection utilizing facial features extracted from the Autistic Children Facial Dataset. Leveraging transfer learning models, including VGG16, ResNet, and Inception, high-level features are extracted from facial images. Additionally, fine-grained details are captured through the utilization of handcrafted image features such as Histogram of Oriented Gradients, Local Binary Patterns, Scale-Invariant Feature Transform, PHASH descriptors. Integration of these features yields three distinct feature vectors, combining image features with VGG16, ResNet, and Inception features. Subsequently, multiple machine learning classifiers, including Random Forest, KNN, Decision Tree, SVM, and Logistic Regression, are employed for ASD classification. Through rigorous experimentation and evaluation, the performance of these classifiers across three datasets is compared to identify the optimal approach for ASD detection. By evaluating multiple classifiers and feature combinations, this work offers insights into the most effective approaches for ASD detection.

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