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.

中文翻译：

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使用深度迁移学习自动进行绵羊面部表情分类

摘要 数字图像识别已被用于生活的不同方面，主要用于物体分类和检测。在自然栖息地通过图像识别监测动物生命对于动物健康和生产至关重要。目前，绵羊疼痛面部表情量表（SPFES）已成为从面部表情监测绵羊的重点。相比之下，根据面部表情估计疼痛程度是动物生命的有效且可靠的标志。然而，人工评估缺乏准确性、耗时且单调。因此，计算机视觉深度学习的最新进展有助于快速准确地对面部表情进行分类。在本文中，我们提出了一个羊脸数据集和框架，它使用带有微调的迁移学习来自动分类正常（无痛）和异常（痛）羊脸图像。当前最先进的基于卷积神经网络 (CNN) 的架构用于训练羊脸数据集。数据增强、L2 正则化和微调已用于准备模型。与绵羊面部表情数据集相关的实验结果使用 VGG16 模型实现了 100% 的训练、99.69% 的验证和 100% 的测试准确率。在使用其他预训练模型的同时，我们获得了 93.10% 到 98.4% 的准确率。因此，它表明我们提出的模型对于正常和异常羊脸的高精度分类是最佳的，并且可以检查全面的数据集。