Cow face identification based on CNN by using channel attention module and spatial attention module

Chengyun Liu; Feiyang Zhao; Boya Huang; Xintong Zhang; Dequan Zhang; Hualin Li

doi:10.1117/12.3026338

27 March 2024 Cow face identification based on CNN by using channel attention module and spatial attention module

Chengyun Liu, Feiyang Zhao, Boya Huang, Xintong Zhang, Dequan Zhang, Hualin Li

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310521 (2024) https://doi.org/10.1117/12.3026338
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Cow face identification plays a crucial role in the cattle management system. Previous studies have primarily focused on radio frequency identification, and a few researchers devote to the cow face identification field. In this paper, instead of solely extracting features from individual images, we have constructed datasets for cow face identification. The datasets include the facial images of an all-black cow, an all-white cow, and a mixed black-and-white cow. We apply the convolutional neural networks method by utilizing ResNet backbone architectures, and additionally, we incorporate different loss functions and attention modules to enhance the model’s capacity. The results demonstrate that our methods have achieved an identification accuracy rate of 97.04% and FRR of 5.06%, which also improves identification speed and performance compared to other studies, marking a notable advancement in cow face identification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengyun Liu, Feiyang Zhao, Boya Huang, Xintong Zhang, Dequan Zhang, and Hualin Li "Cow face identification based on CNN by using channel attention module and spatial attention module", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310521 (27 March 2024); https://doi.org/10.1117/12.3026338

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KEYWORDS

Facial recognition systems

Data modeling

Feature extraction

Education and training

Animals

Convolutional neural networks

Ear

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