  Singular points of a fingerprint image are special locations having high
curvature properties. They can play a pivotal role in fingerprint normalization
and reliable feature extraction. Accurate and efficient extraction of a
singular point plays a major role in successful fingerprint recognition and
indexing. In this paper, a novel deep learning based architecture is proposed
for one shot (end-to-end) singular point detection from an input fingerprint
image. The model consists of a Macro-Localization Network and a
Micro-Regression Network along with three stacked hourglass as a bottleneck.
The proposed model has been tested on three databases viz. FVC2002 DB1_A,
FVC2002 DB2_A and FPL30K and has been found to achieve true detection rate of
98.75%, 97.5% and 92.72% respectively, which is better than any other
state-of-the-art technique.
