Abstract
Minutiae extraction is a crucial step in an automatic fingerprint identification system. However, the presence of noise in poor-quality images causes a large number of extraction errors, including the dropping of true minutiae and production of false minutiae. A study on these errors reveals that postprocessing is effective in removing false minutiae while keeping true ones. Furthermore, the overall processing efficiency could be improved because of the reduction in total minutia number. In this paper, we present a novel fingerprint image postprocessing algorithm. It is developed based on several rules, which are generalized through a study on the errors that commonly occur in minutiae extraction and their effects on the overall verification performance. Thorough experimental tests demonstrate the proposed postprocessing algorithm to be both effective and efficient.
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead
I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.