Nighttime cattle face recognition based on cross-modal shared feature learning

XU Xingshi; WANG Yunfei; DENG Hongxing; SONG Huaibo

doi:10.7671/j.issn.1001-411X.202403020

Journal of South China Agricultural University > 2024 > 45(5): 793-801. > DOI: 10.7671/j.issn.1001-411X.202403020

XU Xingshi, WANG Yunfei, DENG Hongxing, et al. Nighttime cattle face recognition based on cross-modal shared feature learning[J]. Journal of South China Agricultural University, 2024, 45(5): 793-801. DOI: 10.7671/j.issn.1001-411X.202403020

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Nighttime cattle face recognition based on cross-modal shared feature learning

College of Mechanical and Electronic Engineering, Northwest A&F University/Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs/Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China

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Received Date: May 09, 2024
Available Online: June 26, 2024
Published Date: July 14, 2024

Graphical Abstract

Abstract

Abstract

Objective
To address the challenge of effectively recognizing cattle identity in the nighttime, and lay the technical foundation for 24-hour monitoring of cattle.
Method
A nighttime cattle face recognition method based on cross-modal shared feature learning was proposed. The model framework adopted a shallow dual-stream structure to effectively extract shared feature information from different modalities of cattle face images. Additionally, a triplet attention mechanism was introduced to capture intermodal interaction information across dimensions, enhancing the extraction of cattle identity information. Finally, an embedded extension module was utilized to further explore the representation of cross-modal identity information.
Result
The nighttime cattle face recognition model proposed in this article achieved a mean average precision, the first order cumulative matching eigenvalue (CMC-1) and the fifth order cumulative matching eigenvalue (CMC-5) of 90.68%, 94.73% and 97.82% on the test set, respectively. Compared to the model without cross-modality training, the three indexes improved by 19.67, 18.91 and 12.00 percentage points, respectively.
Conclusion
The proposed method provides a reliable solution for nighttime cattle identity recognition, laying a solid technical foundation for the application of continuous 24-hour monitoring of cattle.
- Cattle,
- Identification,
- Heterogeneous face recognition,
- Cross-modality,
- Attention mechanism,
- Shared feature,
- Nighttime

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References

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