A systematic topological method for fingerprint singular point detection

Abstract

Singular point detection is an important issue in fingerprint image analysis. General methods like Poincare index method can detect singular points in non-arch type fingerprints but fail on arch-type fingerprints. Some more sophisticated methods like complex filter method also face the same problem. In this paper, we propose a systematic method for detecting singular points in fingerprint images which utilizes the most fundamental topological feature of acquired fingerprints as the basis for singular point identification. The method differentiates the input fingerprint between arch type and non-arch type. For non-arch type fingerprints singular points are detected as intersection points in a c(>2) level segmentation map. As for arch type fingerprints, singular points are identified from the symmetric line of the fingerprint structure. The method is evaluated using the NIST DB4 database and compared with the complex filter method. The proposed method attains near 90% success rate in detecting singular points and the displacement from ground truth is comparable to that of the complex filter method. Moreover our method is more than once faster in CPU time.