Sheep Face Recognition Model Based on Deep Learning and Bilinear Feature Fusion

SIMPLE SUMMARY: Identifying individual sheep accurately is crucial for establishing precise animal husbandry. In the process of identifying sheep by their faces, changes in sheep face poses and different camera angles can affect the identification accuracy. In this study, we construct a new sheep face recognition model. Sheep face data with different poses and angles are used as input in a bilinear feature extraction network, which extracts the important features of sheep faces separately. Then, a feature fusion method is used to fuse the features extracted by the bilinear network for sheep face recognition. Our experimental results demonstrate that the recognition accuracy of the algorithm is 99.43%, achieving the individual recognition of sheep in complex environments while reducing the influence of pose and angle on recognition. ABSTRACT: A key prerequisite for the establishment of digitalized sheep farms and precision animal husbandry is the accurate identification of each sheep’s identity. Due to the uncertainty in recognizing sheep faces, the differences in sheep posture and shooting angle in the recognition process have an impact on the recognition accuracy. In this study, we propose a deep learning model based on the RepVGG algorithm and bilinear feature extraction and fusion for the recognition of sheep faces. The model training and testing datasets consist of photos of sheep faces at different distances and angles. We first design a feature extraction channel with an attention mechanism and RepVGG blocks. The RepVGG block reparameterization mechanism is used to achieve lossless compression of the model, thus improving its recognition efficiency. Second, two feature extraction channels are used to form a bilinear feature extraction network, which extracts important features for different poses and angles of the sheep face. Finally, features at the same scale from different images are fused to enhance the feature information, improving the recognition ability and robustness of the network. The test results demonstrate that the proposed model can effectively reduce the effect of sheep face pose on the recognition accuracy, with recognition rates reaching 95.95%, 97.64%, and 99.43% for the sheep side-, front-, and full-face datasets, respectively, outperforming several state-of-the-art sheep face recognition models.