A Large Benchmark Dataset for Individual Sheep Face Recognition

• Published in: Agriculture (Basel) Vol. 13; no. 9; p. 1718
• Main Authors: Pang, Yue, Yu, Wenbo, Xuan, Chuanzhong, Zhang, Yongan, Wu, Pei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Access control; Accuracy; agriculture; Algorithms; Animal husbandry; Automation; Benchmarks; Biometry; Breeding; Breeding methods; convolutional neural network; Data collection; dataset; Datasets; Deep learning; ears; Face; Face recognition; Grasslands; Image acquisition; industry; large benchmark; Machine learning; Marking and tracking techniques; Mutton; Neural networks; Pattern recognition; Performance evaluation; Sheep; sheep face recognition; Statistical analysis; Tracking techniques; China; Mongolia
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13091718

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study developed a large-scale benchmark dataset, Sheepface-107, comprising 5350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol was developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.