Melinda MelindaHurriyatul AqifJunidar JunidarMaulisa OktianaNurlida BasirAfdhal AfdhalZulfan Zainal2024-12-132024-12-1320242024-12-13Melinda Melinda, Hurriyatul Aqif, Junidar Junidar, Maulisa Oktiana, Nurlida Basir, Afdhal Afdhal, & Zulfan Zainal. (2024). Image Segmentation Performance using Deeplabv3+ with Resnet-50 on Autism Facial Classification. JURNAL INFOTEL, 16(2), 441–456. https://doi.org/10.20895/infotel.v16i2.11442460-0997302-3210.20895/infotel.v16i2.1144https://oarep.usim.edu.my/handle/123456789/25567https://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1144Jurnal Infotel Volume: 16 No:2 (page: 441-456)In recent years, significant advancements in facial recognition technology have been marked by the prominent use of convolutional neural networks (CNN), particularly in identification applications. This study introduces a novel approach to face recognition by employing ResNet-50 in conjunction with the DeepLabV3 segmentation method. The primary focus of this research lies in the thorough analysis of ResNet-50's performance both without and with the integration of DeepLabV3+ segmentation, specifically in the context of datasets comprising faces of children on the autism spectrum (ASD). The utilization of DeepLabV3+ serves a dual purpose: firstly, to mitigate noise within the datasets, and secondly, to eliminate unnecessary features, ultimately enhancing overall accuracy. Initial results obtained from datasets without segmentation demonstrate a commendable accuracy of 83.7%. However, the integration of DeepLabV3+ yields a substantial improvement, with accuracy soaring to 85.9%. The success of DeepLabV3+ in effectively segmenting and reducing noise within the dataset underscores its pivotal role in refining facial recognition accuracy. In essence, this study underscores the pivotal role of DeepLabV3+ in the realm of facial recognition, showcasing its efficacy in reducing noise and eliminating extraneous features from datasets. The tangible outcome of increased accuracy of 85.9% post-segmentation lends credence to the assertion that DeepLabV3+ significantly contributes to refining the precision of facial recognition systems, particularly when dealing with datasets featuring faces of children on the autism spectrum. Downloadsen-USautism spectrum disorderconvolutional neural networksdeeplabv3+imagesegmentationresnet-50.text::journal::journal article::data paper441456162