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Automated sheep facial expression classification using deep transfer learning

2020Journal article Restricted access
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dc.contributor.authorNoor, Alam
dc.contributor.authorZhao, Yaqin
dc.contributor.authorKoubaa, Anis
dc.contributor.authorWu, Longwen
dc.contributor.authorKhan, Rahim
dc.contributor.authorAbdalla, Fakheraldin Y.O.
dc.date.accessioned2020-10-30T09:30:55Z
dc.date.embargo2120
dc.date.issued2020
dc.description.abstractDigital image recognition has been used in the different aspects of life, mostly in object classification and detections. Monitoring of animal life with image recognition in natural habitats is essential for animal health and production. Currently, Sheep Pain Facial Expression Scale (SPFES) has become the focus of monitoring sheep from facial expression. In contrast, pain level estimation from facial expression is an efficient and reliable mark of animal life. However, the manual assessment is lack of accuracy, time-consuming, and monotonous. Hence, the recent advancement of deep learning in computer vision helps to classify facial expression as fast and accurate. In this paper, we proposed a sheep face dataset and framework that uses transfer learning with fine-tuning for automating the classification of normal (no pain) and abnormal (pain) sheep face images. Current state-of-the-art convolutional neural networks (CNN) based architectures are used to train the sheep face dataset. The data augmentation, L2 regularization, and fine-tuning has been used to prepare the models. The experimental results related to the sheep facial expression dataset achieved 100% training, 99.69% validation, and 100% testing accuracy using the VGG16 model. While employing other pre-trained models, we gained 93.10% to 98.4% accuracy. Thus, it shows that our proposed model is optimal for high-precision classification of normal and abnormal sheep faces and can check on a comprehensive dataset. It can also be used to assist other animal life with high accuracy, save time and expenses.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.compag.2020.105528pt_PT
dc.identifier.issn0168-1699
dc.identifier.urihttp://hdl.handle.net/10400.22/16374
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0168169920306633?via%3Dihub#!pt_PT
dc.subjectCNN architecturespt_PT
dc.subjectFine-tuningpt_PT
dc.subjectSheep face datasetpt_PT
dc.subjectSheep face classificationpt_PT
dc.subjectTransfer learningpt_PT
dc.titleAutomated sheep facial expression classification using deep transfer learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleComputers and Electronics in Agriculturept_PT
oaire.citation.volume175pt_PT
person.familyNameKoubaa
person.givenNameAnis
person.identifier989131
person.identifier.ciencia-idCA19-2399-D94A
person.identifier.orcid0000-0003-3787-7423
person.identifier.scopus-author-id15923354900
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0337d7df-5f77-46a4-8269-83d14bd5ea6b
relation.isAuthorOfPublication.latestForDiscovery0337d7df-5f77-46a4-8269-83d14bd5ea6b

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