Işitme Engelli Çocuklarin Yüz Ifadeleri Tespiti

Muruvvet Bozkurt, Firat Oncel, Cemal Gurpinar, Hatice Kose, Gozde Unal

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

Facial emotions play a critical part in daily life and understanding and analyzing emotions are of great importance in terms of human-computer interaction. Although facial expressions between children and adults differ, many face recognition systems use models that are exclusively trained on adult data. Children with hearing impaired (HI) have hardship in social life due to differences in expressing emotions. Therefore, the facial expressions recognition of children with HI is a challenging problem. This study aims to develop a high performance emotion classification system for children with HI. For training Convolutional Neural Networks (CNN) from scratch, a large amount of data is required, but the children's dataset is limited. Firstly, fine-tuning the model with pre-trained CNN via transfer learning is discussed as a method for the classification of children's emotions. As a result of the experiments, the most accurate result was obtained by fine-tuning the dataset of children with 3 emotion labels with the trained-model of adults with 8 emotion labels.

Tercüme edilen katkı başlığıFacial Expressions Detection of Children with Hearing Impairment
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2022 30th Signal Processing and Communications Applications Conference, SIU 2022
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665450928
DOI'lar
Yayın durumuYayınlandı - 2022
Etkinlik30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey
Süre: 15 May 202218 May 2022

Yayın serisi

Adı2022 30th Signal Processing and Communications Applications Conference, SIU 2022

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???event.eventtypes.event.conference???30th Signal Processing and Communications Applications Conference, SIU 2022
Ülke/BölgeTurkey
ŞehirSafranbolu
Periyot15/05/2218/05/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Keywords

  • convolutional neural networks
  • emotion detection
  • human-machine interaction
  • transfer learning

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