This paper proposes an efficient algorithm to extract the singular points which can be used to classify the given fingerprint. It makes use of a novel algorithm which is a hybrid of orientation field, directional filtering and Poincare Index based algorithms to detect singular points, even when the fingerprint is of low quality or singular point is occluded. Locations of detected singular points are not much accurate and thus they are further refined. Also, some delta points which lie near to the border, may be missed out at the time of detection. Efforts are made to retrieve these missed points. The proposed algorithm also determines the direction of a singular point along with its type (either core or delta). It uses these detected singular points to classify accurately arch, tented arch, left loop, right loop, double loop and whorl type fingerprint patterns. It can handle efficiently the cases of missing delta points during fingerprint classification. The proposed algorithm has been tested on three publicly available databases. It reveals that the proposed algorithm exhibits better singular points detection and fingerprint classification performance in comparison to other well known algorithms. 
A robust singular point detection algorithm