Improving Hard Negative Handling for Individual Dog Re-identication with Facial Biometrics
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Abstract
Visual biometrics, such as facial recognition, play an important role in authentication and identification. This concept extends to animal biometrics, particularly for identifying individual dogs, which is useful for veterinary care, ownership verification, population control, and disease monitoring. However, distinguishing visually similar dogs within the same breed remains challenging due to hard negative samples. In this work, we propose a deep learning model for individual dog re-identification using contrastive learning with a binary cross-entropy loss function. Instead of treating each dog as a separate class, the proposed framework learns a similarity function from positive and negative image pairs, including a high proportion of hard negative pairs during training, to better capture fine-grained facial differences. Experimental results show that the proposed model achieves 96.83% accuracy on hard negative samples and 81.48% accuracy on the public Flickr dog dataset in a zero-shot setting, demonstrating improved performance in same-breed dog re-identification over traditional multiclass classification approaches.
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