Real-time goat face recognition using convolutional neural network

Automatic identification of individual animals is an important step towards achieving accurate breeding histories, significant contributions to breeding and genetic management programmers. Currently, a different type of tags, tattoos, paint brands and microchips are used to uniquely identify livestock animals. However, the manual identification system is time-consuming, expensive and unreliable. In this paper, we present a deep learning approach that aims to fully automated pipeline for face detection and recognition of goats. Due to the high similarity and the lack of adequate dataset this problem is more complex than human face recognition. We composed two different publicly available datasets for detection and recognition. State-of-the-art convolutional neural networks (CNN) model are trained on this dataset. To evaluate the robustness of our approach, we compared it with different face recognition methods. The results show better performance with an accuracy of 96.4%. Furthermore, this paper reports 93%, 83%, 92% and 85% detection accuracy (average precision) for face, eye, nose and ear, respectively. The findings of this research could be helpful to improve animal health and welfare, individual monitoring, activity monitoring and phenotypic data collection. All the dataset and the related outcome are publicly available (https://doi.org/10.17632/4skwhnrscr.2).

Automated summary

This paper presents a deep learning approach for fully automated face detection and recognition of goats. The method shows better performance and accuracy compared to other face recognition methods. The findings of this research are important for animal health and welfare, individual monitoring, activity monitoring, and phenotypic data collection.