Impact of Noisy Singular Point Detection on Performance of Fingerprint Matching

The performance of fingerprint matching has significantly improved in the recent times. However, this performance is still affected by many factors such as inadequate detection of singular points, poor-quality and noisy fingerprint images mostly result in spurious or missing singular points, which generally results in degradation of the overall performance of the fingerprint matching.   This paper presents the impact of noisy or spurious singular (core/delta) points on the performance of fingerprint matching. The algorithm comprises of image enhancement stage, the singular points extraction stage and post-processing stage. The image enhancement stage preprocessed the fingerprint images, the singular point extraction stage extracts the true and the noisy or false singular points, while the post processing stage eliminate the spurious singular point.  Benchmarked FVC2000, FVC2002, FVC2004 and FVC2006 fingerprint databases which comprise four datasets each were used for the experimental study. The completion time for the singular point extraction on each dataset were computed. The matching algorithm was also implemented to verify the impact of noisy singular points on false non match rate (FNMR), false match rate (FMR) and matching speed. The completion time extraction of singular points from the noisy fingerprint images is 263seconds whereas the completion time for extraction of true singular points is 82seconds. The increase in completion time is due to the inclusion of spurious features (noise/contaminants), whereas there is time decreases after the spurious features had been eliminated.  The obtained values and analysis revealed that poor and noisy quality fingerprint images have adverse effect on the performance of fingerprint matching. &nbsp

A digital circuit for extracting singular points from fingerprint images

Since singular point extraction plays an important role in many fingerprint recognition systems, a digital circuit to implement such processing is presented herein. A novel algorithm that combines hardware efficiency with precision in the extraction of the points has been developed. The circuit architecture contains three main building blocks to carry out the three main stages of the algorithm: extraction of a partitioned directional image, smoothing, and searching for the patterns associated with singular points. The circuit processes the pixels in a serial way, following a pipeline scheme and executing in parallel several operations. The design flow employed has been supported by CAD tools. It starts with high-level descriptions and ends with the hardware prototyping into a FPGA from Xilinx