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Automated sheep facial expression classification using deep transfer learning
Ref: CISTER-TR-201014       Publication Date: Aug 2020

Automated sheep facial expression classification using deep transfer learning

Ref: CISTER-TR-201014       Publication Date: Aug 2020

Abstract:
Digital 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.

Authors:
Alam Noor
,
Yaqin Zhao
,
Anis Koubâa
,
Longwen Wu
,
Rahim Khan
,
Fakheraldin Y.O. Abdalla


Published in Computers and Electronics in Agriculture, Elsevier, Volume 175, Article No 105528.
ISSN: 0168-1699.



Record Date: 29, Oct, 2020