علاء نعمة عزيز الانصاري

when studying fingerprint cards some drawbacks were found which showed that one of the main reasons for poor fingerprint quality was a violition of the technique and technology of fingerprinting. Unfortunately, all drawbacks of fingerprint identification systems are more or less inherent in all biometric systems. There is also insufficient protection from imitation of the imprint partly due to the spread of the method. Needless to say that not all scanners can be deceived, but for some people with “unsuitable” fingers (some features of body temperature, wetness) the probability of denial to access can reach 100%. The number of such people varies from the fractions of percent for expensive scanners to ten percent for inexpensive ones. By all means it is worth noting that a large number of drawbacks are caused by the rapid spread of the system, but these drawbacks happen to be and they express themselves very often. In the future biometric devices will continue to be improved becoming more accurate and reliable and most importantly cheaper.

Most organizations require a clear consideration before adopting such system based on their interests on the pros and cons of this system. In addition to this, the government should adopt a strict guide that will assist in limiting biometric data dissemination and related information to such data to hinder the misuse of the rights of an individual by fraudulent fellows such as fingerprint hackers (who engage in fingerprint depiction activities). Adequate level of personal security should highly recommended to this system. The use of fingerprint scanning as a biometric identification system has been a contentious issue. Fingerprint scanners operated on the unimodal biometric system in the early era. Due to the drawbacks related to fingerprint recognition such as fingers with dust or oil and scars not be identified, researchers have designed and developed a multimodal biometric system which have improved the performance and overcome the problems faced because of the unimodal system not been able to easily detect fingers affected with accidental characteristics as mentioned in this work.

В данном исследовании рассматривается улучшенный алгоритм скелетизации отпечатков пальцев. Причиной исследования послужил недостаток современных шаблонных алгоритмов, развивающих идею статьи 1967 года Розенфельда, а именно локальное исчезновение прямых тонких линий. В данном исследовании рассмотрены основные этапы, такие как бинаризация изображения и скелетизация полученного бинарного изображения.

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The orientation field is an important characteristic of human skin patterns and has a significant impact on the results of fingerprint identification. The methods of constructing the orientation field based on the gradient are diverse, but they are united by a high sensitivity to noise and defects that appear on the images during the formation of tracks. The article proposes a new method for constructing a stream field for digital images of fingerprints. The method allows to improve the solution of a number of key tasks of image processing, including the task of predicting the direction of lines in the area of skin folds, scars and other finger surface defects. The method relies on such approaches as image processing at the subpixel level, on cluster analysis of the field of image gradients and consists in the sequential application of several algorithms.

This article is only a one step of fingerprint improvement algorithm research. Trust coefficient, calculated at this article, help us to improve bad fields of fin-gerprint. Visualized results help us to choose parameters for better results. Next steps of this research will relate with estimation of these calculations at benchmarks. So, next benchmark estimates will tell us which of this two filters is better.

An accurate estimated fingerprint orientation fields is a significant step for detection of bad local fields. We can precalculate some im-provements for fingerprint images before matching. There are two steps

As a result of the work, a method for automating search for similar prints was developed and the recognition program was implemented. To sum up the given above we state the following: – the given program allows for an acceptable time to automatically determine the identity of a fingerprint by highlighting peculiar features; – compared to the visual definition on the key areas of the original image, a significant gain in speed and usability was obtained; – the resulting statistical characteristics sufficiently describe the image and allow for recognition with a high degree of accuracy; – the developed subsystem is an integral part of the fingerprint identification system designed to detect the similarity between the two images of the fingerprint; – it helps to establish the identity of the person who applied the finger, which can be used at the entrance to the system; – it is possible to significantly reduce the level of influence of the displacement and transfer of the fingerprint, as well as noise and distortion in the image; – the created system should be considered as a research system designed to identify empirical laws in the domain and further development in the direction of greater automation of the process of identity identification. The developed system implements a new kind of functionality – preparation of images for automated structural analysis.

Through this paper we come out with the conclusion that, for the purpose of verifying the identity of an individual using a fingerprint, the algorithm must pass through several stages, first one Image acquisition (image fingerprint) through sensors. then, Image Processing improving the image like, Gabor filter image processing and fingerprint alternative algorithm image processing, after that fingerprint identification Fingerprint Matching comparing the points on the template, implies obtaining two scanned images of fingerprints, the comparison is performed when the image passed the scanning. Mark the points on the print, then compare them with the general pattern. Decision on identification of fingerprint are taken after comparing the points on the template, thus we obtained the algorithm to prove the identity of the individual using a fingerprint. The next investigation it will be to prepare a study on some of the problems that hinder fingerprint recognition

Researches display that use of a identification fingerprint for is the most convenient biometric identification methods. The probability of an identification error in is much less compared to other biometric methods. In addition, the fingerprint identification device does not require much space on the keyboard or in the mechanism. In most cases, work with important data implies timely decision-making and ongoing management of progress. In this regard, there is a need for continuous confirmation of identity (if a person for some reason leaves his workplace, then anyone at this time will be able to give instructions to tele- or managing teams). This identity verification method of “the single sign – on” cannot provide security in this case, and entering the password after each command is unnecessarily complicated. Although there are ready-made systems on the market, along with their advantages they have a number of disadvantages, such as the closeness of the source code and the algorithm, as a result of it, limited applications of these systems, as well as high price. As a result, it makes sense to develop a system that would provide an opportunity for all developers to have a ready base for developing their own projects based on biometric technologies. As well as to provide an object description of variety, not only of papillary pattern images. We are designing a system of both search and research nature, and aim at facilitating the development of image processing algorithms, simplifying the analysis of experimental data and revealing common patterns.

The main goal of the study is to devise a novel technique for image segmentation particularly tailored for lowquality images. We introduce the CLAHE technique which is a mixture of different methods that make feature delineation effective even in unfavorable lighting conditions and complicated backgrounds due to poor image quality. Gaussian smoothing helps in preserving significant edges while reducing noise; Canny edge detection guarantees precise edge detection based on gradient magnitude and direction; morphological operations further refine detected edges by eliminating small noise clusters. The new approach is far better than traditional methods and significantly boosts the accuracy of segmentation an indication of how much this study contributes to the field of image processing with innovative techniques. We present an innovative composite technique marrying contrast enhancement, noise reduction, and edge detection under a single umbrella. This novel methodology is designed to address the shortcomings inherent in many traditional approaches by its ability to dynamically adjust to diverse image conditions. This adaptability makes our system a versatile tool that can be applied across various fields such as object recognition, biometric analysis or medical imaging without breaking a sweat. In the future work, the combination of CLAHE with Canny edge detection supported by morphological operations irrespective of the type of noise and contrast situations is expected to provide better handling not only does it propose itself as a pragmatic solution but also an efficient one for any other case that might surface later down the road. We are not only looking at improving adaptability through an adaptive framework of future images based on machine learning (which would just make our algorithm more adaptive) but also taking into consideration the level and nature of noise or quality degradation in those images we aim much higher. higher. This paper introduces a new approach in image processing that deals with three main aspects contrast enhancement, noise reduction, and edge detection all under one unified methodological framework. When standard techniques fail due to poor-quality images, the proposed approach is able to adapt on-the-fly based on the specific conditions of the image in real-time: a solution that sees itself playing a reliable role across different applications like object recognition or biometric systems and even medical imaging requirements. Addressing CLAHE and Canny edge detection with morphological operations each designed to handle specific types of noise and particular scenarios in contrast situations this is an innovative strategy towards these two highly distinct components through their conjoined process for the first time ever unveiled. Next steps for research should focus on improving adaptability possibly through machine learning empowering the algorithm to better cope at different levels of noise complexities and varied image degradations it might encounter at runtime.