Towards on-farm pig face recognition using convolutional neural networks

Mark Hansen, Melvyn Smith, Lyndon Smith, Michael G. Salter, Emma M. Baxter, Marianne Farish, Bruce Grieve

    Research output: Contribution to journalArticlepeer-review


    Identification of individual livestock such as pigs and cows has become a pressing issue in recent years as intensification practices continue to be adopted and precise objective measurements are required (e.g. weight). Current best practice involves the use of RFID tags which are time-consuming for the farmer and distressing for the animal to fit. To overcome this, non-invasive biometrics are proposed by using the face of the animal. We test this in a farm environment, on 10 individual pigs using three techniques adopted from the human face recognition literature: Fisherfaces, the VGG-Face pre-trained face convolutional neural network (CNN) model and our own CNN model that we train using an artificially augmented data set. Our results show that accurate individual pig recognition is possible with accuracy rates of 96.7% on 1553 images. Class Activated Mapping using Grad-CAM is used to show the regions that our network uses to discriminate between pigs.
    Original languageEnglish
    Pages (from-to)145-152
    JournalComputers in Industry
    Early online date21 Mar 2018
    Publication statusPublished - Jun 2018


    • Pig face recognition
    • Deep learning
    • convolutional neural network
    • Biometrics


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