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

FLAG : the fault-line analytic graph and fingerprint classification

Fingerprints can be classified into millions of groups by quantitative measurements of their new representations - Fault-Line Analytic Graphs (FLAG), which describe the relationship between ridge flows and singular points. This new model is highly mathematical, therefore, human interpretation can be reduced to a minimum and the time of identification can be significantly reduced. There are some well known features on fingerprints such as singular points, cores and deltas, which are global features which characterize the fingerprint pattern class, and minutiae which are the local features which characterize an individual fingerprint image. Singular points are more important than minutiae when classifying fingerprints because the geometric relationship among the singular points decide the type of fingerprints. When the number of fingerprint records becomes large, the current methods need to compare a large number of fingerprint candidates to identify a given fingerprint. This is the result of having a few synthetic types to classify a database with millions of fingerprints. It has been difficult to enlarge the minter of classification groups because there was no computational method to systematically describe the geometric relationship among singular points and ridge flows. In order to define a more efficient classification method, this dissertation also provides a systematic approach to detect singular points with almost pinpoint precision of 2x2 pixels using efficient algorithms

An accurate fingerprint reference point determination method based on curvature estimation of separated ridges

This paper presents an effective method for the detection of a fingerprint’s reference point by analyzing fingerprint ridges’ curvatures. The proposed approach is a multi-stage system. The first step extracts the fingerprint ridges from an image and transforms them into chains of discrete points. In the second step, the obtained chains of points are processed by a dedicated algorithm to detect corners and other points of highest curvature on their planar surface. In a series of experiments we demonstrate that the proposed method based on this algorithm allows effective determination of fingerprint reference points. Furthermore, the proposed method is relatively simple and achieves better results when compared with the approaches known from the literature. The reference point detection experiments were conducted using publicly available fingerprint databases FVC2000, FVC2002, FVC2004 and NIST

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods