  The proof of origin of logs is becoming increasingly important. In the
context of Industry 4.0 and to combat illegal logging there is an increasing
motivation to track each individual log. Our previous works in this field
focused on log tracking using digital log end images based on methods inspired
by fingerprint and iris-recognition. This work presents a convolutional neural
network (CNN) based approach which comprises a CNN-based segmentation of the
log end combined with a final CNN-based recognition of the segmented log end
using the triplet loss function for CNN training. Results show that the
proposed two-stage CNN-based approach outperforms traditional approaches.
