Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. 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. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed.Īlong 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%. 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 methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. 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. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. Systems of the Internet of Things are actively implementing biometric systems. While there is no conclusively best modality, recommendations of usage for each modality were provided. The advantages and disadvantages of using each of these modalities during the first year of life were compared, based on both qualitative assessments of usage, and quantitative assessments of performance.
LOCKIT UCSD SOFTWARE
For the fingerprint modality, novel hardware and image processing software were developed to acquire fingerprints from infants, and convert the images into a format which is backward compatible with existing international standards for minutiae extraction and comparison. For the iris modality, existing hardware was used to acquire the images, while adjustments to the existing preprocessing algorithms were applied to cater for the localisation and segmentation of infant irises. For the ear modality, existing hardware and software which have previously been applied to adults were applied to children. Where necessary, novel hardware or software was developed. Where possible, the performance of existing hardware and software that was developed for adults was assessed with infants. Each modality provides different challenges. In order to begin the development of biometric recognition systems for children, researchers collected fingerprint, iris, and outer ear shape biometric information from infants. Solving this challenge could protect children from identity theft and identity fraud, help in reuniting lost children with their parents, improve border control systems in combatting child trafficking, and assist in electronic record-keeping systems. However, the biometric recognition of children is an unsolved challenge. Biometric recognition is often used for adults for a variety of purposes where an individual’s identity must be ascertained.
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