Feature fusion capsule network for cow face recognition

Abstract

There are many difficulties in cow face recognition, including variable individual poses, challenges in collecting individual data, complex image backgrounds, etc. An improved capsule network (CapsNet) was used to solve these problems. First, we combined convolutional and local binary pattern (LBP) texture features with a feature extractor named C-LBP. Then, by utilizing the self-attention module, the feature extraction capability was enhanced. An intermediate capsule layer was added to improve capsules utilization. We tested our model on datasets of cow faces. According to the experimental data, the proposed model improved the structure of the original CapsNet. In the middle of the training, positional data was added, giving the model a higher performance and greater resilience in cow face recognition.