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 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 a 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