老化不变的绵羊通过特征脱进行面部识别.
Suhui Liu1,2, Chuanzhong Xuan1,2, Zhaohui Tang1
1College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Animals : an open access journal from MDPI
|August 14, 2025
概括
这项研究引入了面部识别的终身生物识别学习 (LBL),开发了一个网络,适应面部特征的变化,随着绵羊的成长. 这种新方法在识别随时间推移的单个绵羊方面取得了很高的准确性.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
背景情况:
- 准确识别单个绵羊对于智能农业和畜牧管理至关重要.
- 基于深度学习的羊面部识别提供无接触识别,但在成长过程中难以改变面部特征.
- 由于随着时间的推移,面部的动态变化导致现有模型的准确性下降,这限制了实际应用.
研究的目的:
- 开发一个强大的羊面部识别系统,能够持续适应面部动态变化.
- 为了应对长期在养殖绵羊中保持识别准确性的挑战.
- 引入终身生物识别学习 (LBL) 机制,用于绵羊的个体识别.
主要方法:
- 提出了绵羊面孔网络 (LBL-SheepNet) 的终身生物识别学习,这是一个特征脱网络.
- 构建了一个数据集,由55只羊的31,200张图像组成,每月跟踪从1到12个月大.
- 采用了多模块框架,包括Squeeze-and-Excitation (SE) 模块,非线性特征解与混合道批量关注,以及针对年龄不变特征提取的对抗性学习.
主要成果:
- 在羊脸数据集上,LBL-SheepNet实现了95.5%的识别准确度和95.3%的平均精度.
- 特征脱网络成功地分离了与年龄相关的和特定身份的面部特征.
- 对抗性学习有效地抑制了年龄偏差特征干扰,专注于年龄不变的标识符.
结论:
- 拟议的LBL-SheepNet有效地减轻了因在成长中的绵羊中面部动态变异引起的识别准确度下降.
- 这项研究证明了特征脱网络与对抗性年龄不变学习相结合的有效性,用于长期的个人识别.
- 这种终身生物识别学习方法提高了农业环境中羊面部识别系统的性能和实用性.
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