Biometric identification of sheep via a machine-vision system

Almog Hitelman, Yael Edan, Assaf Godo, Ron Berenstein, Joseph Lepar, Ilan Halachmi

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This paper describes a sheep biometric identification system based on facial images. A machine vision system and deep learning model were developed and applied for animal identification. The system included two 8-MegaPixels cameras installed in a controlled water trough adapted to work with NVIDIA Jetson Nano-embedded system-on-module (SoM). Data from 81 Assaf breed sheep, aged two to three months, from two different groups of sheep, were collected over a period of two weeks. The biometric identification model included two steps: face detection and classification. In order to locate and localize the sheep's face in an image, the Faster R-CNN deep learning object detection algorithm was applied. The detected face was provided as input to seven different classification models. Different transfer learning methods were examined. The best performance was obtained using a ResNet50V2 model with the state-of-art ArcFace loss function. The identification system resulted in average accuracies of 95% for the two groups tested. When applying transfer learning methods, average identification accuracies improved to 97% in both groups, and the training process was accomplished in half the time. The newly developed system proves the feasibility of individual biometric identification of sheep on commercial farms.

Original languageEnglish
Article number106713
JournalComputers and Electronics in Agriculture
StatePublished - 1 Mar 2022


  • Convolutional Neural Network
  • Deep learning
  • Facial recognition
  • Lamb
  • Small ruminants

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture


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