Fingerprint reference point detection for image retrieval based on symmetry and variation

Reference point plays an important role in fingerprint identification systems. The reference point is widely used for the fingerprint retrieval in large-scale databases. This paper proposes a novel algorithm for detecting a convex core point as a unique reference point consistently and accurately for all types of fingerprints. In order to detect robust core point candidates, a modified complex filter, called semi-radial symmetry filter, is proposed to detect correctly rotational symmetries of core points. Moreover, a vertical orientation variation feature, called VORIV feature, is proposed to remove spurious core points and concave core points. Therefore, the proposed technique computes the Variation and Symmetry Combined Energy (VSCOME). Then, the reference point is located by searching the global VSCOME maximum. The experimental results on the public database (FVC2004 DB1 set A) show that the proposed technique exhibits a very high robustness and gets the best performance in comparing with other approaches in literature.

► A novel reference point detection algorithm is proposed for fingerprint retrieval.
► A semi-radial symmetry filter can detect rotational symmetries of core points.
► VORIV describes the vertical orientation variation of neighborhood blocks.
► VORIV provides more discriminating features of convex core points.
► The Variation and Symmetry Combined Energy of the reference point is global maximum.