Liu Suhui, Xuan Chuanzhong, Tang Zhaohui, Wang Guangpu, Gao Xinyu, Wang Zhipan
College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Inner Mongolia Engineering Research Center for Intelligent Facilities in Prataculture and Livestock Breeding, Hohhot 010018, China.
Animals (Basel). 2025 Aug 6;15(15):2299. doi: 10.3390/ani15152299.
Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the growth of sheep, their facial features keep changing, which poses challenges for existing sheep face recognition models to maintain accuracy across the dynamic changes in facial features over time, making it difficult to meet practical needs. To address this limitation, we propose the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network designed for continuous adaptation to ovine facial changes, and constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age. The LBL-SheepNet model addresses dynamic variations in facial features during sheep growth through a multi-module architectural framework. Firstly, a Squeeze-and-Excitation (SE) module enhances discriminative feature representation through adaptive channel-wise recalibration. Then, a nonlinear feature decoupling module employs a hybrid channel-batch attention mechanism to separate age-related features from identity-specific characteristics. Finally, a correlation analysis module utilizes adversarial learning to suppress age-biased feature interference, ensuring focus on age-invariant identifiers. Experimental results demonstrate that LBL-SheepNet achieves 95.5% identification accuracy and 95.3% average precision on the sheep face dataset. This study introduces a lifelong biometric learning (LBL) mechanism to mitigate recognition accuracy degradation caused by dynamic facial feature variations in growing sheep. By designing a feature decoupling network integrated with adversarial age-invariant learning, the proposed method addresses the performance limitations of existing models in long-term individual identification.
精确识别单个绵羊样本对于实施智能农业平台和优化畜群管理系统起着关键作用。随着深度学习技术的发展,绵羊面部识别为单个绵羊的识别提供了一种高效且非接触式的解决方案。然而,随着绵羊的生长,它们的面部特征不断变化,这给现有的绵羊面部识别模型在随着时间推移面部特征动态变化时保持准确性带来了挑战,难以满足实际需求。为了解决这一限制,我们提出了绵羊面部网络的终身生物特征学习(LBL-SheepNet),这是一种为持续适应绵羊面部变化而设计的特征解耦网络,并构建了一个数据集,该数据集包含来自55只绵羊从1月龄到12月龄每月跟踪的31200张图像。LBL-SheepNet模型通过多模块架构框架解决绵羊生长过程中面部特征的动态变化。首先,一个挤压激励(SE)模块通过自适应通道校准增强判别性特征表示。然后,一个非线性特征解耦模块采用混合通道-批次注意力机制将与年龄相关的特征与特定身份特征分离。最后,一个相关性分析模块利用对抗学习抑制年龄偏差特征干扰,确保专注于年龄不变的标识符。实验结果表明,LBL-SheepNet在绵羊面部数据集上实现了95.5%的识别准确率和95.3%的平均精度。本研究引入了一种终身生物特征学习(LBL)机制,以减轻生长中绵羊动态面部特征变化导致的识别准确率下降。通过设计一个集成对抗年龄不变学习的特征解耦网络,所提出的方法解决了现有模型在长期个体识别中的性能限制。