To detect the core point more accurately and quickly has always been the focus for the fingerprint recognition. In this paper, we propose a novel core point detecting algorithm with global information, core point detection from global feature (CPGF). Firstly, we extract a set of points with high curvature according to the statistics of the fingerprint orientation distribution. Secondly, a reference line is fitted on the point set with certain orientation distribution. Finally, the core point is detected by the Poincare Index around the reference line. The experimental results demonstrated that our algorithm is low time-consuming and it is able to produce convincing core point coordinates from the ROI provided by the reference line which is valuable to be investigated for further optimizing other core point algorithms.
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