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<p>Cattle face recognition technology holds significant potential in applications such as livestock insurance, traceability, and real-time monitoring. However, existing methods predominantly focus on single-breed identification, limiting their applicability in multi-breed farms and complex scenarios. This paper proposes an innovative multi-breed cattle face recognition method leveraging image enhancement and deep learning. We introduce an iterative least squares pyramid (ILS-pyramid) algorithm to enhance cattle face images, effectively filtering out hair noise and redundant details. An efficient multi-scale attention-based detection model (EMA-YOLOv8) accurately detects cattle faces in complex backgrounds. Subsequently, an improved global attention residual network (IGAM-iResNet) extracts robust feature vectors for final recognition. Experimental results on a dataset of eight cattle breeds from Jilin Province, China, demonstrate our method’s exceptional performance, achieving a comprehensive accuracy rate of 99.846%, thus setting a new benchmark in multi-breed cattle face recognition. The open-source code and dataset facilitate replication and further research in this domain. The code is available at: <a href="https://github.com/xindw211/cattle-face-recognition">https://github.com/xindw211/cattle-face-recognition</a>. The datasets used in this article can be obtained from the following URL: <a href="https://pan.baidu.com/s/1wHKT-6R6B9OtxaYZ2N4MOw?pwd=6ckt">https://pan.baidu.com/s/1wHKT-6R6B9OtxaYZ2N4MOw?pwd=6ckt</a>.</p>

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Enhanced multi-breed cattle face recognition in complex environments using attention-based deep learning

  • 期刊论文

  • 发表日期：

    2025年5月3日

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  • Zhiguo Xiao,
  • Wenxin Dai,
  • Changgen Li,
  • Wenhui Liang,
  • Xianqing Chen

摘要

Cattle face recognition technology holds significant potential in applications such as livestock insurance, traceability, and real-time monitoring. However, existing methods predominantly focus on single-breed identification, limiting their applicability in multi-breed farms and complex scenarios. This paper proposes an innovative multi-breed cattle face recognition method leveraging image enhancement and deep learning. We introduce an iterative least squares pyramid (ILS-pyramid) algorithm to enhance cattle face images, effectively filtering out hair noise and redundant details. An efficient multi-scale attention-based detection model (EMA-YOLOv8) accurately detects cattle faces in complex backgrounds. Subsequently, an improved global attention residual network (IGAM-iResNet) extracts robust feature vectors for final recognition. Experimental results on a dataset of eight cattle breeds from Jilin Province, China, demonstrate our method’s exceptional performance, achieving a comprehensive accuracy rate of 99.846%, thus setting a new benchmark in multi-breed cattle face recognition. The open-source code and dataset facilitate replication and further research in this domain. The code is available at: https://github.com/xindw211/cattle-face-recognition. The datasets used in this article can be obtained from the following URL: https://pan.baidu.com/s/1wHKT-6R6B9OtxaYZ2N4MOw?pwd=6ckt.