Color-Adaptive Sheep Face Recognition Method Based on Retinex-Guided Gradient Perception Convolution

Reliable individual identification is essential for precision livestock farming. To reduce the performance disparity among coat-color groups in open-set sheep face recognition, this study proposes a color-adaptive recognition method. A coat color classifier routes images to dedicated recognition models, each equipped with a tailored preprocessing pipeline. The method introduces a Retinex-Guided Gradient Perception Convolution (RGPConv), which embeds Retinex physical priors into the convolution operator and generates spatially adaptive modulation maps from the illumination component to dynamically control gradient enhancement intensity, enabling illumination-aware adaptive gradient feature extraction. A Multi-scale Channel Attention Fusion (MCAF) mechanism is further designed to integrate multi-level semantic information. Experiments on the LSFW dataset show that the proposed method achieves an open-set recognition accuracy of 96.08%, outperforming the baseline Li-SheepFaceNet by 1.50 percentage points. Compared with the corresponding routing-only subset baselines, accuracy improves by 2.41 and 2.00 percentage points for black-faced and white-faced sheep, respectively. On the cross-scene RealSheepFace and cross-species GoatFace datasets, accuracy improves by 4.26 and 2.00 percentage points, suggesting potential cross-domain transferability.