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Updated: Sep 11, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Aging-Invariant Sheep Face Recognition Through Feature Decoupling.

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
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Summary
This summary is machine-generated.

This study introduces lifelong biometric learning (LBL) for sheep face recognition, developing a network that adapts to changing facial features as sheep grow. The new method achieves high accuracy in identifying individual sheep over time.

Keywords:
agricultural engineeringbiometric recognitiondeep learningfeature decouplingsheep facial features

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Area of Science:

  • Agricultural Technology
  • Computer Vision
  • Biometrics

Background:

  • Accurate identification of individual sheep is crucial for smart agriculture and herd management.
  • Deep learning-based sheep face recognition offers contactless identification but struggles with changing facial features during growth.
  • Existing models face accuracy degradation due to dynamic facial variations over time, limiting practical application.

Purpose of the Study:

  • To develop a robust sheep face recognition system capable of continuous adaptation to dynamic facial changes.
  • To address the challenge of maintaining identification accuracy in growing sheep over extended periods.
  • To introduce a lifelong biometric learning (LBL) mechanism for ovine individual identification.

Main Methods:

  • Proposed the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network.
  • Constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age.
  • Employed a multi-module framework including Squeeze-and-Excitation (SE) module, nonlinear feature decoupling with hybrid channel-batch attention, and adversarial learning for age-invariant feature extraction.

Main Results:

  • LBL-SheepNet achieved 95.5% identification accuracy and 95.3% average precision on the sheep face dataset.
  • The feature decoupling network successfully separated age-related and identity-specific facial characteristics.
  • Adversarial learning effectively suppressed age-biased feature interference, focusing on age-invariant identifiers.

Conclusions:

  • The proposed LBL-SheepNet effectively mitigates recognition accuracy degradation caused by dynamic facial variations in growing sheep.
  • The study demonstrates the efficacy of a feature decoupling network integrated with adversarial age-invariant learning for long-term individual identification.
  • This lifelong biometric learning approach enhances the performance and practicality of sheep face recognition systems in agricultural settings.