Work

Identity-Preserving Generative Alignment for Longitudinal Sheep Face Recognition Across Growth Stages

Year: 2026

Type: article

Abstract: Reliable individual identification is essential for precision livestock management, but sheep facial appearance changes substantially during growth, weakening non-contact recognition. This study evaluated whether early-life facial images can support longitudinal identification at later growth stages. A monthly face dataset of 30 Small-tailed Han sheep was collected from 1 to 12 months of age. Images from months 1–8, 9–10 and 11–12 were used for training, validation and independent testing, respectively. An identity-preserving generative alignment framework was developed to reduce growth-related appearance mismatch while maintaining discriminative facial information. On the independent test set, the proposed method achieved 95.6% Top-1 accuracy and 93.9% macro-F1, outperforming convolutional, augmentation-based, feature-alignment, CycleGAN-based and Transformer baselines. Verification analysis achieved an area under the receiver operating characteristic curve (AUC) of 0.978, an equal error rate (EER) of 4.2%, and a true accept rate (TAR) of 94.5% at a false accept rate (FAR) of 1 × 10−4. Genuine similarity decreased with growth interval but remained separated from impostor similarity. Robustness tests covering time gaps, device changes, viewpoint shifts and image perturbations showed remaining deployment challenges. A mobile prototype achieved 32-bit floating point (FP32) and 16-bit floating point (FP16) latencies of 210 and 150 ms.

Source: Agriculture

DOI: 10.3390/agriculture16151586

Authors: Suhui Liu, Guangpu Wang, Aiqing Tian, Chuanzhong Xuan

Institutions: Inner Mongolia Agricultural University, Southwest Jiaotong University

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137