Background:
Systems of the Internet of Things are actively implementing biometric systems. For fast and
high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the
stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor
with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results.
Methods:
The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the
skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration
showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor
function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately
form the surroundings where the skeleton is formed.
Results:
Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has
been developed, the recognition results of which have better quality, which allows to increase the recognition quality from
3 to 10%.
Conclusion:
The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during
which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight
neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to
white if certain conditions are satisfied. This Ateb-Gabor filtering will provide better performance, as it allows to obtain
more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the
proposed method.
Automatic Correction of Ma and Sonka's Thinning Algorithm Using P-Simple Points
