FURI | Spring 2022
Secure Tracing With Dendrite Vision: Computer Vision Methods For Matching Dendritic Identifiers Within The Supply Chain
Dendrites are fascinating branching structures that exhibit randomness, yet they are unique, non-repeatable, and identifiable with the right algorithmic innovations. The question this research aims to answer is how can machine learning and these naturally random structures be brought together to create a highly accurate, secure, and specific identifier? In order to create a model with high dendrite identification accuracy, a pre-trained model was fine-tuned over an original dataset consisting of unique dendrites from multiple angles. The model’s success suggests that dendrites can be implemented on a large scale within the supply chain.
Hometown: Phoenix, Arizona, United States
Graduation date: Spring 2025