Robust Fingerprint Matching Using Spiral Partitioning Scheme

Fingerprint matching for low quality or partial fingerprint images is very challenging. It is mainly because the features such as minutia points can not be extracted reliably. In the case of partial fingerprint images captured using solid state sensors, enough number of minutia points may not be included. In this paper, we introduce a novel fingerprint representation that combines information from each extracted minutia with detected ridges in its neighborhood. The proposed algorithm first enhances a fingerprint image and generates a binary image. Then instead of using thinning-based algorithms, the ridges are extracted using a chaincode scheme, which retains the original thickness of the ridges and precise local orientations. The minutia points are detected by tracing the ridge lines. Finally the enriched local structural features are built for each minutia by a spiral coding using the ridge line orientations around the minutia. The new features are translation and rotational invariant. Each feature vector represents a minutia and its neighboring ridge structures. Matching of two fingerprints is performed by calculating the Euclidean distances between pairs of corresponding feature vectors. Preliminary experiments show that the proposed algorithm is effective.