Cattle AutoID: Biometric for Cattle Identification: Cattle AutoID
8
Issued Date
2023-10-24
Resource Type
Scopus ID
2-s2.0-85182398335
Journal Title
ACM International Conference Proceeding Series
Start Page
570
End Page
574
Rights Holder(s)
SCOPUS
Bibliographic Citation
ACM International Conference Proceeding Series (2023) , 570-574
Suggested Citation
Kusakunniran W., Phongluelert K., Sirisangpaival C., Narayan O., Thongkanchorn K., Wiratsudakul A. Cattle AutoID: Biometric for Cattle Identification: Cattle AutoID. ACM International Conference Proceeding Series (2023) , 570-574. 574. doi:10.1145/3626641.3627215 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/95728
Title
Cattle AutoID: Biometric for Cattle Identification: Cattle AutoID
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Existing solutions of animal identification (i.e., cattle in this research project) are based on RFID, ear tag, and microchip. However, they are facing with difficulties of high cost, dislodged and lost, and harm to human operators and animals. Therefore, this paper proposes a biometric based solution of cattle identification using cattle's face images. The proposed method is developed using a convolutional neural network (CNN) for both main steps of face localization and face recognition. The face localization model is trained using a Single-Shot Detector (SSD) architecture, where the face recognition model is trained based on FaceNet. The proposed method is validated using our dataset containing 2,432 cattle images from 152 different cattle. It achieves 94.74% and 83.45% for subject-based and image-based accuracies respectively.