Publication:
Performance Of Shufflenet And Vgg-19 Architectural Classification Models For Face Recognition In Autistic Children

Simple item page
dc.contributor.authorMelinda Melinda
dc.contributor.authorMaulisa Oktiana
dc.contributor.authorYudha Nurdin
dc.contributor.authorIndah Pujiati
dc.contributor.authorMuhammad Irhamsyah
dc.contributor.authorNurlida Basir
dc.date.accessioned2024-06-16T06:35:39Z
dc.date.available2024-06-16T06:35:39Z
dc.date.issued2023
dc.date.submitted2024-1-23
dc.descriptionInternational Journal on Advanced Science, Engineering and Information Technology, Volume 13 Issue 2 Page (674–680)
dc.description.abstractThis study discusses the face recognition of children with special needs, especially those with autism. Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects social skills, ways of interacting, and communication disorders. Facial recognition in autistic children is needed to help detect autism quickly to minimize the risk of further complications. There is extraordinarily little research on facial recognition of autistic children, and the resulting system is not fully accurate. This research proposes using the Convolution Neural Network (CNN) model using two architectures: ShuffleNet, which uses randomization channels, and Visual Geometry Group (VGG)-19, which has 19 layers for the classification process. The research object used in the face recognition system is secondary data obtained through the Kaggle site with a total of 2,940 image data consisting of images of autism and non-autism. The faces of autistic children are visually difficult to distinguish from those of normal children. Therefore, this system was built to recognize the faces of people with autism. The method used in this research is applying the CNN model to autism face recognition through images by comparing two architectures to see their best performance. Autism and non-autism data are grouped into training data, 2,540, and test data, as much as 300. In the training stage, the data was validated using validation data consisting of 50 autism image data and 50 non-autism image data. The experimental results show that the VGG-19 has high accuracy at 98%, while ShuffleNet is 88%.
dc.identifier.citationMelinda Melinda, Maulisa Oktiana , Yudha Nurdin, Indah Pujiati, Muhammad Irhamsyah, Nurlida Basir (2023). Performance of ShuffleNet and VGG-19 Architectural Classification Models for Face Recognition in Autistic Children. International Journal on Advanced Science, Engineering and Information Technology, 13(2), 674–680. https://doi.org/10.18517/ijaseit.13.2.18274
dc.identifier.doi10.18517/ijaseit.13.2.18274
dc.identifier.epage680
dc.identifier.issn2088-5334
dc.identifier.issue2
dc.identifier.other302-29
dc.identifier.spage674
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/19912
dc.identifier.urihttps://ijaseit.insightsociety.org/index.php/ijaseit/article/view/18274/pdf_2403
dc.identifier.volume13
dc.language.isoen_US
dc.publisherINSIGHT - Indonesian Society for Knowledge and Human Development
dc.relation.ispartofInternational Journal on Advanced Science, Engineering and Information Technology
dc.relation.issn2088-5334
dc.relation.journalInternational Journal on Advanced Science, Engineering and Information Technology
dc.subjectFace recognition system
dc.subjectautism
dc.subjectConvolutional Neural Network (CNN)
dc.subjectShuffleNet
dc.subjectVGG-19.
dc.titlePerformance Of Shufflenet And Vgg-19 Architectural Classification Models For Face Recognition In Autistic Children
dc.typetext::journal::journal article
dspace.entity.typePublication
oaire.citation.endPage680
oaire.citation.issue2
oaire.citation.startPage674
oaire.citation.volume13
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliationUniversiti Sains Islam Malaysia

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Performance of ShuffleNet and VGG-19 Architectural Classification Models for Face Recognition in Autistic Children
Size:
1.27 MB
Format:
Adobe Portable Document Format
Download

Collections

Scopus