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Face Detection and Identification
Konôpková, Júlia ; Drahanský, Martin (referee) ; Váňa, Jan (advisor)
This work is focused on the problematic of face detection and identification in photography. The introduction is devoted to the most popular methods with briefly descriptions of their principles and rules. Within the practical part of this work we implement and test on free available databases the several of these methods. In the conclusion we evaluate the results and addition of this whole work.
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Multicameras Biometric Gateway to Identify People
Kosík, Dominik ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This thesis is about creating biometric gate to identify people. The Identification is achieved with 5 RGB cameras and one thermal camera. Thermal camera is used for detection of person. Then, from images acquired from RGB cameras, is created 3D model of photographed person. This model is then used for the identification. However due to inaccuracies in created model, identification isn't precise enough. Because of that, it's necessary to modify used algorithms processing 3D model, so better precision is achieved.
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Image processing using Android device
Korchakov, Sergei ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
This master’s Thesis focuses on image processing on Android platform and development of an application, that is able to do face detection and recognition in real scene. Thesis gives highlight of modern algorithms of face detection. It first examines and compares the standard features of Android platform (FaceDetector a FaceDetectionListener) and JJIL, OpenIMAJ, OpenCV libraries experiment, and presents the results. For purposes of face recognition was selected OpenCV library. Three different algorithms of identification were tested: FisherFaces, EigenFaces a Local Binary Patterns Histograms. Based on performance comparison best methods were implemented in developed application.
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Person Identification
Ťapuška, Tomáš ; Zuzaňák, Jiří (referee) ; Hradiš, Michal (advisor)
This master's thesis is about the most known methods for face recognition. There are described their advantages and disadvantages. This work is specialized at holistic methods for face recognition, which are working with 2D pictures of people. I implemented the automatic system for face recognition according to digital picture of face. There was, in this system, implemented these methods: KNN (K nearest neighbour), PCA (Principal component analysis) and LDP (Linear doscriminant projection). There was done some tests to compare implemented methods. The tests was done on the pictures from dataset FERET. In the conclusion of this text are considered implemented approaches and is marked the best method for face recognition from implemented.
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Face Identification
Částek, Petr ; Jaša, Petr (referee) ; Chmelař, Petr (advisor)
This document is trying to introduce the reader with issues of identifying the face connected with miscellaneous scanning technologies and enviroments. Inside this document there are mentioned some possibilities of creation unique print of a face so that there would be denied unwanted effects of enviroment and the identification of persons would be possible.
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Video database for face feature recognition
Stříteský, Jan ; Říha, Kamil (referee) ; Vlach, Jan (advisor)
This work compares face databases freely accessible on the Internet which are suitable for the testing of developed algorithms for facial features recognition in a picture. In the course of the work’s project a new face video database was created, encompassing a total of 51 samples. The video database includes a simple application suitable for searching through its contents. In order to limit the size of the video database, compression of individual picture samples was conducted. The created video database can be implemented for testing algorithms for facial features recognition (e.g. face detection in a video, identification of a person, detection of eye blinking, speech recognition).
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Person Identification and Verification Using EEG
Žitný, Roland ; Orság, Filip (referee) ; Tinka, Jan (advisor)
The aim of this work was to create a brain-computer interface that reliably identifies and verifies a person using his electroencephalographic signals. Creating a user profile and verifying it is based on processing reactions to his own face, and the face of strangers or acquaintances. Algorithms such as bandpass and noise removal using wavelet transformation are user to filter signals. The classification of reactions is performed using a convolutional neural network or linear discriminant analysis. The average accuracy of the linear discriminant analysis is 66.2 % and of the convolutional neural network is 58.7 %. The maximum achieved accuracy was with linear discriminant analysis and at 93.7 %.
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Automatic Face Recognition in Real Environment
Kičina, Pavol ; Šmirg, Ondřej (referee) ; Přinosil, Jiří (advisor)
This master‘s thesis describes the identification faces in real terms. It includes an overview of current methods of detection faces by the classifiers. It also includes various methods for detecting faces. The second part is a description of two programs designed to identify persons. The first program operates in real time under laboratory conditions, where using web camera acquires images of user's face. This program is designed to speed recognition of persons. The second program has been working on static images, in real terms. The main essence of this method is successful recognition of persons, therefore the emphasis on computational complexity. The programs I used a staged method of PCA, LDA and kernel PCA (KPCA). The first program only works with the PCA method, which has good results with respect to the success and speed of recognition. In the second program to compare methods, which passed the best method for KPCA.
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Face superresolution from image sequence
Mezina, Anzhelika ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
Táto práce se zabývá použitím hlubokého učení neuronových sítí ke zvýšení rozlišení obrázků, které obsahují obličeje. Tato metoda najde uplatnění v různých oblastech, zejména v bezpečnosti, například, při bezpečnostním incidentu, kdy policie potřebuje identifikovat podezřelého z nahraného videa ze sledovací kamery. Cílem této práce je navrhnout minimálně dvě architektury neuronových sítí, které budou pracovat se sekvencí snímků, a porovnat je s metodami zpracování jediného snímku. Pro tento účel je také vytvořena nová trénovací množina, obsahující sekvenci snímku obličeje. Metody zpracování jednoho snímku jsou natrénované na nové množině. Dále jsou navrženy nové metody zvětšení obrázků na základě sekvence snímků. Tyto metody jsou založené na U-Net modelu, který je úspěšný v segmentaci, ale také v superrozlišení. Pro zlepšení architektury byly použity reziduální bloky a jejich modifikace, a navíc také percepční ztrátová funkce, která dovoluje vyhnout se rozmazání a získání více detailů. První čast této práce je věnovana popisu neuronových sítí a některých architektur, jejichž modifikace mohou být použity v superrozlišení. Druhá část se poté zabývá popisem metod pro zvýšení rozlišení obrazu pomocí jednoho snímku, několika snímků a videa. Ve třetí části jsou popsány navržené metody a experimenty a v poslední části porovnaná metod založených na jednom snímku a několika snímcích. Navržené metody jsou schopny získat více detailů v obraze, ale mohou produkovat artefakty. Ty lze ale poté eliminovat pomocí filtru, například Gaussova. Nové metody méně selhávají při detekci obličejů, a to je podstatné u identifikace člověka v případě incidentu.
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Biometric Gateway Using Camera to Identify People
Jelen, Vilém ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
Biometric gateways are used to quickly and accurately identify people. Of the biometric characteristics, iris, face and fingerprints are commonly used. By combining them, better identification results can be achieved. The aim of this thesis is to create such a biometric gateway together with the control application. A combination of iris of both eyes and face is used, which is captured by cameras from three angles to increase accuracy. Neural networks are used to detect and extract face features. Iris recognition is realized using Daugman's algorithm.
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