|
|
Detekce živosti a identifikace dlaně
Kala, Adam ; Goldmann, Tomáš (oponent) ; Sakin, Martin (vedoucí práce)
Tato bakalářská práce je zaměřena na detekci živosti lidské ruky a její identifikaci z multispektrálních snímků. Hlavním cílem práce je rozpoznávání živé ruky a jejích falzifikátů na základě námi získaných informací, nález markantů pro identifikaci dlaně a jejich použití. V práci jsou popsány techniky na ořez zájmové oblasti, na extrakci charakteristických znaků z dlaně a následná detekce živosti a identifikace osoby. Úspěšnost detekce živosti dosáhla přes 90% a identifikace osob s mírou chybného přijetí (FAR) menší než 2% pro zvolené datové sady.
|
|
|
Person Recognition Based on Anthropometric Proportions
Šimún, Samuel ; Semerád, Lukáš (oponent) ; Goldmann, Tomáš (vedoucí práce)
This bachelor thesis focuses on the potential use of biometrics and machine learning. It discusses in detail anthropometric characteristics, their evolution and the potential of using these characteristics for person identification. It analyzes the potential uses of machine learning and what technologies exist for extracting a person's pose from an image for outputting the pose in the second dimension and in the third dimension. Also, the thesis describes and implements specific methods that use anthropometric features for person identification.
|
|
|
Aplikace pro autentizaci osob na základě snímků sítnice
Moncz, Oliver ; Goldmann, Tomáš (oponent) ; Kavetskyi, Andrii (vedoucí práce)
Tato práce se zaobírá problémem autentizace osob na základě snímků sítnice. Hlavním cílem je navrhnout algoritmus, který dokáže porovnat snímek sítnice s registrovanými snímky v databázi. Výstupem je potom určení totožnosti s mírou jistoty. Dalším cílem bylo vytvořit aplikaci, která přes jednoduché grafické uživatelské prostředí umožní provádět zmíněné operace. Nakonec jsou vyhodnoceny výsledky a porovnány s již existujícími řešeními. Navržený autentizační systém dosáhl průměrné přesnosti 72,46 % pro datasety DRIVE a STARE, pro dataset FIRE dosáhl 78,9 %.
|
|
|
Složení snímku prstu s viditelným krevním řečištěm.
Pekárek, Jakub ; Semerád, Lukáš (oponent) ; Rydlo, Štěpán (vedoucí práce)
Práce popisuje zpracování snímku prstu pro vytvoření kompozitního snímku s viditelným krevním řečištěm. Krevní řečiště je detekováno algoritmem Maximum Curvature. Následně bylo experimentováno s hledáním a spojování klíčových bodů pomocí algoritmů SIFT a ORB. Protože však algoritmy nepřinášely požadované výsledky, musí být klíčové body vyznačovány manuálně. Z nich je vytvořen odhad matice transformace. Pomocí odhadu matice transformace je spojena dvojice snímků. Tento cyklus přiřazení klíčových bodů a spojení snímků probíhá i pro nově vytvořené snímky. Po spojení posledních dvou snímků je výsledkem kompozitní snímek s viditelným krevním řečištěm. Tento kompozitní snímek může být dále využit pro verifikaci nebo identifikaci osob.
|
|
|
Biometric fingerprint liveness detection
Rišian, Lukáš ; Smital, Lukáš (oponent) ; Vítek, Martin (vedoucí práce)
This bachelor thesis deals with the problem of biometric fingerprint liveness recognition. The aim of the thesis is to propose a solution that reliably and securely recognizes fake fingerprints from genuine ones. Specifically, the work focuses on investigating methods for detecting fingerprint liveness using software tools, creating a custom fingerprint test database, testing and identifying relevant characteristics for successful liveness detection, and using them to implement fingerprint liveness recognition algorithms. Another goal was to create a GUI to provide a tool for overall detection. The work includes an analysis of the basics of biometrics, fingerprint characteristics and structure, current sensors used for fingerprint extraction, databases used, image preprocessing methods, tested features, implemented algorithms, and two GUI variants. More than 180 different image features were tested and more than 20 variants of algorithms were implemented. From these algorithms, the best ones were selected, whose detection results were then compared with those of foreign authors. The best algorithm achieved an accuracy of almost 90%, which can be considered a reliable and satisfactory result compared to foreign authors.
|
|
|
New technologies for biometric recognition based on hand characteristics
Dvořák, Michal ; Jadlovský, Ján (oponent) ; Marcialis, Gian Luca (oponent) ; Drahanský, Martin (vedoucí práce)
Biometrics allows us to identify or verify people based on their physical characteristics. Although the recognition itself may be very fast, extracting the chosen biometry features in the real world is a non-trivial task. To perform a recognition on a large number of people, for example at an airport, we need to perform the scanning and recognition in the shortest possible time, ideally without the user having to stand still. In this dissertation thesis, I focus on the development of a touchless and therefore hygienic biometric system, capable of performing identification, feature extraction and subsequent recognition on the fly using hand characteristics, and other systems and algorithms needed to solve this complex problem. This thesis presents the current state of research and development in the field of biometric identification based on selected hand characteristics, with a focus on identification using hand geometry and fingerprints. Further it also discusses the technological principles used for the collection of the characteristics. According to the dissertation objectives, formulated on the basis of the research, the practical part of this thesis deals with the development of biometric systems for use to identify large numbers of people in flight, for which most current biometric systems based on hand characteristics are not suitable. The thesis then addresses related issues, in particular presentation attack detection and biometric multimodality.
|
|
|
Connection of algorithms for removal of influence of skin diseases on the process for fingerprint recognition
Heidari, Mona ; Derawi, Mohammad (oponent) ; Gomez-Barrero, Marta (oponent) ; Drahanský, Martin (vedoucí práce)
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.
|
|
|
Rozpoznávání obličeje ve videu a jeho využití v policejní práci
Fabián, Jan ; Šťastná, Dagmar (oponent) ; Šedrlová, Magdalena (vedoucí práce)
Cílem této bakalářské práce je popsat technologii rozpoznávání obličejů a její využití v praxi. Zejména v posledních letech se technologie rozpoznávání obličejů stala velmi diskutovaným tématem, ať už ve spojitosti s rozšířením této technologie v Číně nebo díky potenciálu této technologie pro bezpečnostní složky. Na jednu stranu technologie rozpoznávání obličejů přináší spoustu nových možností v oblasti prevence kriminality, ale na druhou stranu s sebou také nese riziko narušení soukromí jednotlivých osob. Tato práce se zabývá historií této technologie, metodami, jenž tato technologie používá a také popisuje příklady využití technologie rozpoznávání obličejů v praxi, spolu s riziky spojenými s uvedením této technologie do praxe.
|
|
| |
|
|
Liveness Detection on Fingers Using Vein Pattern
Dohnálek, Tomáš ; Váňa, Jan (oponent) ; Drahanský, Martin (vedoucí práce)
This work presents liveness detection extension of the Touchless Biometric Systems 3D-Enroll fingerprint sensor which is based on finger vein pattern. Hardware solution was designed and realized using infrared diodes. Designed software system operates in two different modes: liveness detection based on texture features and user verification using finger vein matching. A dataset containing more than 1,100 images of both real fingers and their falsifications was gathered. Performance of both proposed modes was evaluated using mentioned dataset and suitable materials, that can fool the liveness detection module, were highlighted.
|
host ::
RSS.