Electronics 2025, 14(12), 2343; https://doi.org/10.3390/electronics14122343 (registering DOI) - 7 Jun 2025
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Cattle face recognition is a feasible way for identification of cattle in information management of large farms or identity verification in commercial insurance for farms. Recent cattle face recognition approaches, based on supervised learning, heavily depend on annotation which is both labor-intensive and
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Cattle face recognition is a feasible way for identification of cattle in information management of large farms or identity verification in commercial insurance for farms. Recent cattle face recognition approaches, based on supervised learning, heavily depend on annotation which is both labor-intensive and time-consuming. Unsupervised learning for cattle face recognition aims at learning discriminative representations for cattle retrieval from unlabeled data. However, the inherent noise in pseudo-labels significantly hinders the performance. Thus, we propose an unsupervised learning framework with part-attention-based pseudo-label refinement reciprocal compact loss (USL-PARC) to enhance the reliability of the pseudo-label by the fine-grained local context derived via attention mechanism, while obtaining separable and discriminative features by contrastive learning with the compact loss. Firstly, we propose a part-attention-based pseudo-label refinement framework to refine the pseudo-labels of global features by dynamically supplementing local fine-grained information, thereby mitigating the effects of pseudo-label noise. Secondly, ResNet-Sim network, augmented with the SimAM attention mechanism, is constructed to strengthen the ability of capturing more informative localized supplementary information. Finally, we raise compact loss to increase the tightness of the clustering of feature points from the same identity in the feature space. It is encouraging to find that USL-PARC achieves 97.4% accuracy, outperforming the state-of-the-art unsupervised learning models on our CattleFace2025 dataset. These results demonstrate the effectiveness of our proposed USL-PARC on mitigating the impact of pseudo-label noise and enhancing the learning ability of separable and discriminative features.
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