A new approach to biometric wood log traceability combining traditional methods and deep learning

This paper focuses on the biometric traceability of oak logs using images of cross-sections. The images were acquired in two temporally separated image acquisition sessions, and we want to find the correct matches for the images of the second session from the pool of images of the first session. No biometric traceability method has yet been proposed for oak logs and they differ greatly from softwood logs. In this context, we present a new method consisting of two steps. The first one involves extracting visible features from log-end images using the Scale-Invariant Feature Transform (SIFT) method and the SuperPoint architecture. Then, in the second step, the extracted features are matched to verify whether two images correspond to the same log. For this, we consider the deep neural network LightGlue that is well-known for efficiently matching sparse local features between pairs of images. This new approach was compared with two recent state-of-the-art methods, including a significant evolution of one of them. The experiments were carried out on two datasets, including a new and large dataset of almost 25k images. The results show the performance of the new method for identifying oak logs, significantly outperforming the most recent ones. Source code and pre-trained models are available at https://github.com/ Braquemarok/ATECH2024, while the image database is accessible at https://doi.org/10.57745/9DBCL4.