Biometric identification of sheep via a machine-vision system

Hitelman, Almog; Edan, Yael; Godo, Assaf; Berenstein, Ron; Lepar, J.; Halachmi, I.

doi:10.1016/j.compag.2022.106713

Cited by 39 publications

(21 citation statements)

References 30 publications

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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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Section: Referencementioning

confidence: 99%

When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

Yang

et al. 2022

Agriculture

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“…Hitelman et al [3] proposed to use ResNet-50V2 network with ArcFace loss function for sheep face identification. They performed their system on a database of 81 Assaf breed sheep.…”

Section: Animal Face Recognitionmentioning

confidence: 99%

Biometric-Based Security System for Smart Riding Clubs

et al. 2022

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Horses, workers or riders need safety in a farm or a riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and recognize the identity of each worker or rider and horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse as well as the human gait biometrics due to their simplicity and intuitiveness in an uncontrolled environment. This work suggests a Siamese network based on DenseNet features for human gait recognition, a Sparse Neural Network (SNN) based on sparse features for horse face detection and a horse face recognition method based on Gabor features, LDA and SVM. Because of the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. The proposed systems achieved an average accuracy of gait recognition equal to 95% in the 0°view, 100% in the 90°view and 98.90% in the 180°view on Casia-B dataset, an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.INDEX TERMS Horse face detection, human gait recognition, neural network, siamese network, smart riding club, THDD.

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“…Among them, face detection is to detect the location of a face in an image; face normalizing is to align the faces to normalized coordinates; and face identification is implemented on normalized faces [6]. Most livestock face recognition studies focus on face identification, and there are some studies that focus on face detection, such as Shuang Song [8], who detected sheep faces with Pruning-Based YOLOv3; Billah et al [4] detected the goat face location with YOLO V4; Hitelman et al [9] detect the sheep face location through Faster RCNN; and Wang and Liu [10] detected pig face location through EfficientDet-D0. However, no study was found on livestock face normalization, and livestock face recognition technology is still in the research stage and has not yet been applied in livestock farms.…”

Section: Introductionmentioning

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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Biometric identification of sheep via a machine-vision system

Cited by 39 publications

References 30 publications

When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

Biometric-Based Security System for Smart Riding Clubs

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

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