Nighttime cattle face recognition based on cross-modal shared feature learning

Objective To address the challenge of effectively recognizing cattle identity in the nighttime, and lay the technical foundation for 24-hour monitoring of cattle.

Method A nighttime cattle face recognition method based on cross-modal shared feature learning was proposed. The model framework adopted a shallow dual-stream structure to effectively extract shared feature information from different modalities of cattle face images. Additionally, a triplet attention mechanism was introduced to capture intermodal interaction information across dimensions, enhancing the extraction of cattle identity information. Finally, an embedded extension module was utilized to further explore the representation of cross-modal identity information.

Result The nighttime cattle face recognition model proposed in this article achieved a mean average precision, the first order cumulative matching eigenvalue (CMC-1) and the fifth order cumulative matching eigenvalue (CMC-5) of 90.68%, 94.73% and 97.82% on the test set, respectively. Compared to the model without cross-modality training, the three indexes improved by 19.67, 18.91 and 12.00 percentage points, respectively.

Conclusion The proposed method provides a reliable solution for nighttime cattle identity recognition, laying a solid technical foundation for the application of continuous 24-hour monitoring of cattle.