Authors - Mohd Hizami Ab Halim, Suriani Mohd Sam, Norliza Mohamed, Hazilah Mad Kaidi, Norulhusna Ahmad Abstract - Accurate identification of fish species based on morphometric and meristic characteristics is challenging, particularly for commercially important species such as Megalaspis Cordyla, due to subtle morphological differences and limited labelled data. This review examines recent advances in deep metric learning, with a focus on Siamese network architectures, for few-shot morphometric and meristic identification of M. Cordyla. We synthesize studies on metric-based similarity learning, landmark-driven morphometric analysis, and finegrained fish classification to show how Siamese networks effectively learn discriminative embedding spaces under low-data conditions. The review also analyzes reported performance comparisons across the literature, including classification accuracy, precision-recall behavior, robustness to small training sets, and generalization to unseen species or populations. Overall, the findings indicate that Siamese and deep metric learning-based approaches consistently outperform conventional classification models in fine-grained fish identification tasks, while highlighting open challenges such as the lack of standardized morphometric datasets for Megalaspis Cordyla, limited meristic-aware benchmarking, and the need for interpretable similarity measures to support fisheries science and biodiversity conservation.
