Cross-Angle Facial Individual Verification of Giant Panda

Enhancing the protection of giant pandas necessitates a precise means of verifying their individual information. With the development of deep learning technology, some individual recognition methods for giant pandas have emerged. However, these methods often neglect cross-angle facial recognition, a common occurrence in natural settings, and predominantly focus on identification rather than verification, rendering them unsuitable for wild populations of unknown individuals. Cross-angle facial verification poses novel challenges, notably feature misalignment and geometric deformation induced by varying angles, particularly in the verification process reliant on comparing feature discrepancies. To address this issue, we developed the CrossPandaFace model. This model employs a Pixel Drift Unit (PDU) to adjust feature pixels and utilizes template features generated by the Template Generation Module (TGM) as a reference to align giant pandas from different angles onto a unified template. Furthermore, Multi-scale Feature Supplement (MSFS) compensates for the potential risk of losing local features of aligned features. Experimental results show state-of-the-art performance, thus affirming the efficacy of our model for cross-angle giant panda verification.