Automated salamander recognition using deep neural networks and feature extraction

  • Jørgen Bakløkken
  • Felix Schoeler
  • Hugo Nørholm
  • Sony George
  • Marius Pedersen
  • Børre Dervo


This paper presents a study conducted to recognize salamanders by using their unique body markings based on images. The detection and matching of unique patterns in a salamander’s body can be complex due variability in individual animals size, shape, orientation and also influence from the external enviornment. While traditional methods require time intensive manual image corrections of the salamanders to achieve accurate recognition, in this work we propose a fully automatic techinque for straigthening. We also propose a matching technique based on the corrected images. The convolutional neural network ResNet50 and dense scale-invariant feature transform (DSIFT) are used for belly pattern localization, and matching for salamander recognition.