Thesis Details
Connection of algorithms for removal of influence of skin diseases on the process for fingerprint recognition
This thesis focuses on data structures, image processing, and computer vision methods for detecting and recognizing diseases in fingerprint images. The number of developed biometric systems and even used biometric characteristics is increasing.
It is widely accepted that an individual's fingerprint is unique and remains relatively unchanged throughout life. However, the structure of these ridges can be changed and damaged by skin diseases. As these systems depend heavily on the structure of an individual's fingertip ridge pattern that positively determines their identity, people suffering from skin diseases might be discriminated against as their ridge patterns may be impaired. Likely, fingerprint devices have not been designed to deal with damaged fingerprints; therefore, after scanning the fingerprint, they usually reject it.
The influence of skin disease is an important but often neglected factor in biometric fingerprint systems. An individual might be prevented from using specific biometric systems when suffering from a skin disease that affects the fingertips.
Collecting a database of fingerprints influenced by skin diseases is a challenging task. It is expensive and time-consuming, but it also requires the assistance of medical experts and the ability to find willing participants suffering from various skin conditions on fingertips.
The raw diseased fingerprint database is first analyzed to provide a solid foundation for future research. Common signs among all fingerprint images affected by the disease are found for every particular disease, and a general description of each disease and its influences is defined. Then we automatically assign the label based on a combination of the known state of the fingerprint image.
The proposed solution is integrated with different algorithms focused on image processing libraries and computer vision methods for object detection. The solution has been evaluated on damaged fingerprint datasets and highlights the state of the art implementations using proposed techniques.
The state of the art technique for disease detection implementations uses texture analysis and feature detection by comparing the intensity values of pixels in a small neighborhood in an image.
Due to the complexity of each disease pattern, the combination of texture analysis algorithms leads to better detection results.
The combination of GLCM, LBP, orientation field, and mathematical morphology can detect damage (artifacts) in fingerprint images.
Combining these features makes it possible to identify changes in the texture and shape of the fingerprint flow caused by diseases. These techniques capture different aspects of the texture and shape of the damage in fingerprint images and lead to identifying changes in the texture caused by diseases. In the stages of the detection process, mathematical morphology operations are applied to improve the structural details by removing small irregularities in the image and simplify the shape of objects, making it easier to identify and isolate them expanding the boundaries of objects in an image or filling gaps and connect broken parts of objects, leading to better object detection and recognition.
At the end of the detection process, coherence is applied to show the quality evaluation of fingerprint image patches into three types healthy, damaged, and background.
Overall, the proposed solution showcases the effectiveness of integrating multiple image processing and computer vision algorithms for disease detection in fingerprint images.\ The combination of these algorithms can accurately detect and localize disease patterns in damaged fingerprint datasets, thus providing a reliable solution for disease detection in forensic applications.
Diseased fingerprint detection; biometrics; computer vision methods; signal processing; pattern recognition; fingerprint database.
@phdthesis{FITPT981,
author = "Mona Heidari",
type = "Ph.D. thesis",
title = "Connection of algorithms for removal of influence of skin diseases on the process for fingerprint recognition",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2023,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/phd-thesis/981/"
}