  Animal Re-ID is crucial for wildlife conservation, yet it faces unique
challenges compared to person Re-ID. First, the scarcity and lack of diversity
in datasets lead to background-biased models. Second, animal Re-ID depends on
subtle, species-specific cues, further complicated by variations in pose,
background, and lighting. This study addresses background biases by proposing a
method to systematically remove backgrounds in both training and evaluation
phases. And unlike prior works that depend on pose annotations, our approach
utilizes an unsupervised technique for feature alignment across body parts and
pose variations, enhancing practicality. Our method achieves superior results
on three key animal Re-ID datasets: ATRW, YakReID-103, and ELPephants.
