Facial recognition using deep learning has been widely used in social life for applications such as authentication, smart door locks, and photo grouping, etc. More and more networks have been developed to facilitate computer vision tasks, such as ResNet, DenseNet, EfficientNet, ConvNeXt, and Siamese networks. However, few studies have systematically compared the advantages and disadvantages of such neural networks in identifying individuals from images, especially for pet animals like cats. In the present study, by systematically comparing the efficacy of different neural networks in cat recognition, we found traditional CNNs trained with transfer learning have better performance than models trained with the fine-tuning method or Siamese networks in individual cat recognition. In addition, ConvNeXt and DenseNet yield significant results which could be further optimized for individual cat recognition in pet stores and in the wild. These results provide a method to improve cat management in pet stores and monitoring of cats in the wild.

翻译：深度学习人脸识别已广泛应用于社会生活中的身份验证、智能门锁及照片分组等场景。为促进计算机视觉任务，已开发出越来越多的网络架构，如ResNet、DenseNet、EfficientNet、ConvNeXt及孪生网络等。然而，现有研究鲜有系统比较此类神经网络在图像个体识别中的优劣，尤其针对猫等宠物动物。本研究通过系统比较不同神经网络在猫识别中的效能，发现采用迁移学习训练的传统卷积神经网络在个体猫识别任务中表现优于微调方法训练的模型及孪生网络。此外，ConvNeXt与DenseNet取得了显著效果，可进一步优化以应用于宠物店及野外环境中的个体猫识别。这些结果为改进宠物店猫只管理与野外猫群监测提供了有效方法。