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Pig Face Recognition Based on Metric Learning by Combining a Residual Network and Attention Mechanism

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  • Rong Wang

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    College of Information Engineering, Northwest A&F University, Yangling, Xianyang 712100, China)

  • Ronghua Gao

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Qifeng Li

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Jiabin Dong

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

Abstract

As machine vision technology has advanced, pig face recognition has gained wide attention as an individual pig identification method. This study establishes an improved ResNAM network as a backbone network for pig face image feature extraction by combining an NAM (normalization-based attention module) attention mechanism and a ResNet model to probe non-contact open-set pig face recognition. Then, an open-set pig face recognition framework is designed by integrating three loss functions and two metrics to finish the task with no crossover of individuals in the training and test sets. The SphereFace loss function with the cosine distance as a metric and ResNAM are combined in the framework to obtain the optimal open-set pig face recognition model. To train our model, 37 pigs with a total of 12,993 images were randomly selected from the collected pig face images, and 9 pigs with a total of 3431 images were set as a test set. 900 pairs of positive sample pairs and 900 pairs of negative pairs were obtained from the images in the test set. A series of experimental results show that our accuracy reached 95.28%, which was 2.61% higher than that of a human face recognition model. NAM was more effective in improving the performance of the pig face recognition model than the mainstream BAM (bottleneck attention module) and CBAM (convolutional block attention module). The research results can provide technological support for non-contact open-set individual recognition for intelligent farming processes.

Suggested Citation

  • Rong Wang & Ronghua Gao & Qifeng Li & Jiabin Dong, 2023. "Pig Face Recognition Based on Metric Learning by Combining a Residual Network and Attention Mechanism," Agriculture, MDPI, vol. 13(1), pages 1-17, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:144-:d:1026625
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    Citations

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    Cited by:

    1. Gang Liu & Hao Guo & Alexey Ruchay & Andrea Pezzuolo, 2023. "Recent Advancements in Precision Livestock Farming," Agriculture, MDPI, vol. 13(9), pages 1-3, August.
    2. Fang Wang & Xueliang Fu & Weijun Duan & Buyu Wang & Honghui Li, 2023. "Visual Detection of Lost Ear Tags in Breeding Pigs in a Production Environment Using the Enhanced Cascade Mask R-CNN," Agriculture, MDPI, vol. 13(10), pages 1-15, October.

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