Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

Xu, Feng; Gao, Jing; Pan, Xin

doi:10.1109/access.2022.3182806

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…In the field of animal research, a small dataset may be required to be used for face recognition for some reasons [46,47]. In order to perform better in small datasets, LAD-RCNN integrates some dataset enhancement functions, such as horizontal flip, vertical flip, 90 • rotation, 2 × 2 merger, and 3 × 3 merger.…”

Section: Discussionmentioning

confidence: 99%

LAD-RCNN: A Powerful Tool for Livestock Face Detection and Normalization

Sun

Liu

Yang

et al. 2023

Animals

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With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in the area of animal face detection and face identification was conducted. However, there are no specialized studies on livestock face normalization, which may significantly reduce the performance of face identification. The keypoint detection technology, which has been widely applied in human face normalization, is not suitable for animal face normalization due to the arbitrary directions of animal face images captured from uncooperative animals. It is necessary to develop a livestock face normalization method that can handle arbitrary face directions. In this study, a lightweight angle detection and region-based convolutional network (LAD-RCNN) was developed, which contains a new rotation angle coding method that can detect the rotation angle and the location of the animal’s face in one stage. LAD-RCNN also includes a series of image enhancement methods to improve its performance. LAD-RCNN has been evaluated on multiple datasets, including a goat dataset and infrared images of goats. Evaluation results show that the average precision of face detection was more than 97%, and the deviations between the detected rotation angle and the ground-truth rotation angle were less than 6.42° on all the test datasets. LAD-RCNN runs very fast and only takes 13.7 ms to process a picture on a single RTX 2080Ti GPU. This shows that LAD-RCNN has an excellent performance in livestock face recognition and direction detection, and therefore it is very suitable for livestock face detection and normalization.

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Section: Discussionmentioning

confidence: 99%

LAD-RCNN: A Powerful Tool for Livestock Face Detection and Normalization

Sun

Liu

Yang

et al. 2023

Animals

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“…Animal Algorithm (Technique) Accuracy Kumar et al [2] Cattle Cattle; face recognition; animal identification; RFID; RANSAC 92.50% Cai and Li [3] Cattle LBP; WLBP; MBP 67.70-95.30% Santoni et al [4] Cattle CNN; GLCM; e-Livestock Indonesia; cattle 93.12% Chen et al [5] Cow VGG16; Deep Learning 85.45% Xu et al [6] Cow Capsule network; cow face recognition; Siamese neural network 96.87%…”

Section: Authorsmentioning

confidence: 99%

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

Liu,

Zhao,

Huang

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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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.

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“…Dataset Method Accuracy (%) [14] 81 sheep CNN 95.00% [15] 28 pigs CNN 96.80% [16] over 5000 images of 547 sheep CNN 85.00% [17] 3278 pictures of goats CNN 96.40% [18] 945 images of cow faces CNN 91.67% [13] 1553 images of 10 pigs CNN 96.70% [19] 2364 images of pigs CNN 83.00% [20] 2318 images of 90 cows CNN 91.30%…”

Section: Referencementioning

confidence: 99%

When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

Yang

et al. 2022

Agriculture

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Sheep face recognition models deployed on edge devices require a good trade-off between model size and accuracy, but the existing recognition models cannot do so. To solve the above problems, this paper combines Mobilenetv2 with Vision Transformer to propose a balanced sheep face recognition model called MobileViTFace. MobileViTFace enhances the model’s ability to extract fine-grained features and suppress the interference of background information through Transformer to distinguish different sheep faces more effectively. Thus, it can distinguish different sheep faces more effectively. The recognition accuracy of 96.94% is obtained on a self-built dataset containing 5490 sheep face photos of 105 sheep, which is a 9.79% improvement compared with MobilenetV2, with only a small increase in Params (the number of parameters) and FLOPs (floating-point operations). Compared to models such as Swin-small, which currently performs SOTA, Params and FLOPs are reduced by nearly ten times, whereas recognition accuracy is only 0.64% lower. Deploying MobileViTFace on the Jetson Nano-based edge computing platform, real-time and accurate recognition results are obtained, which has implications for practical production.

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Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

Cited by 10 publications

References 30 publications

LAD-RCNN: A Powerful Tool for Livestock Face Detection and Normalization

LAD-RCNN: A Powerful Tool for Livestock Face Detection and Normalization

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

When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

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