Comparison of deep-learning-based face recognition characteristics across multiple animal species

Deep learning has achieved strong performance in human face recognition, but animal identification remains challenging due to limited data and large inter-species variation. This study evaluates deep face recognition across five species using ArcFace and transfer learning. Experiments show that transferring from large-scale datasets, such as humans and cattle, significantly improves accuracy for small-scale species, raising chimpanzees, goats, and horses accuracy to 0.85, 0.94, and 0.93, respectively. Grad-CAM analysis further indicates that transfer learning stabilizes attention on meaningful facial regions. These results highlight transfer learning as an effective solution for robust animal face recognition under data-scarce conditions.