An Improved Method for Fingerprints’ Singular Points Detection based on Orientation Field Partition

As a global feature of fingerprints, singular point plays important roles in fingerprint model, synthesis fingerprint, fingerprint classification, fingerprint alignment and so on. In our previous work, a rapid and effective fingerprint singular points detection method was proposed. That method detects singular points based on partitioning the orientation field into a serious non-overlapping homogenous areas. However, for the same orientation field, different partition schemes may lead to different singular point extraction results, especially for poor quality fingerprint images. For eliminating the uncertainty, we employ three different partition schemes simultaneously for each orientation field, and combine the three extraction results to make the final decision. Experiment proves that: this method is more accuracy than the previous one.