A Large Benchmark Dataset for Individual Sheep Face Recognition

Yue Pang, Wenbo Yu, Chuanzhong Xuan, Yongan Zhang and Pei Wu ()
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Yue Pang: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Wenbo Yu: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Chuanzhong Xuan: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Yongan Zhang: College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Pei Wu: College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Agriculture, 2023, vol. 13, issue 9, 1-18

Abstract: 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.

Keywords: sheep face recognition; large benchmark; deep learning; convolutional neural network; dataset (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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