Bottlenose dolphin identification using synthetic image-based transfer learning

The Indo-Pacific bottlenose dolphin (IPBD) ( Tursiops aduncus ) is a key species in marine ecosystems. Photo-identification (photo-ID) is a fundamental method for studying dolphin populations by identifying individuals based on the unique features of their dorsal fins. Despite recent developments in learning-based photo-ID algorithms, the lack of training data for these models has become a bottleneck for improving the accuracy of these algorithms. In this study, we used synthetic image generation and deep learning to improve photography-based IPBD identification. We generated 7500 synthetic dorsal fin images of 30 dolphins and trained a custom triplet neural network using ResNet50 to distinguish individuals. The model achieved 84.8 % accuracy within the top 10-ranked positions and 72.2 % accuracy in the top 5-ranked positions, demonstrating the potential of these technologies to enhance IPBD monitoring and conservation efforts. • Synthetic image-based transfer learning overcomes data scarcity challenges. • Novel triplet neural network improves fine-grained feature learning on dolphin photo ID. • Flexible model handles new individuals without retraining, enabling long-term scalability.