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Dataset generation for specific cases of face recognition
Kolmačka, Tomáš ; Kolařík, Martin (referee) ; Rajnoha, Martin (advisor)
The diploma thesis deals with current problems of person identification and deep learning. Furthermore, the work deals mainly with obtaining quality and diverse data that are used to train deep learning with convolutional neural networks for face recognition. There is very little public access to such data, so the practical part focuses on creating the MakeHuman plugin that will generate a database of random face images. It is possible to generate faces according to five different scenarios in which purely random faces or faces where the same can be seen with modifications such as different hair, beard, hat, glasses and more are created. The scenarios also allow you to generate faces with some expressions or faces as they age. You can set some parameters that give the appearance of the resulting database in the plugin. This can include face images from different angles of rotation, zooming and lighting.
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Face Identification
Macenauer, Oto ; Drozd, Michal (referee) ; Chmelař, Petr (advisor)
This document introduces the reader to area of face recognition. Miscellaneous methods are mentioned and categorized to be able to understand the process of face recognition. Main focus of this document is on issues of current face recognition and possibilities do solve these inconveniences in order to be able to massively spread face recognition. The second part of this work is focused on implementation of selected methods, which are Linear Discriminant Analysis and Principal Component Analysis. Those methods are compared to each other and results are given at the end of work.
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Face detection system
Karásek, Miroslav ; Horák, Karel (referee) ; Petyovský, Petr (advisor)
This work deals with methods of computer vision for localization faces in an image segmentation of individual parts of face and its comparison with the face in the reference frame. The paper gives a brief overview of biometric methods useful to identify people. It describes the various methods of locating faces in images and their features. It also deals with the editing of digital images, with the design of my own methods of localization and verification of faces and the subsequent implementation of these methods.
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Descriptor for Identification of a Person by the Face
Coufal, Tomáš ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Thesis provides an overview and discussion of current findings in the field of biometrics. In particular, it focuses on facial recognition subject. Special attention is payed to convolutional neural networks and capsule networks. Thesis then lists current approaches and state-of-the-art implementations. Based on these findings it provides insight into engineering a very own solution based of CapsNet architecture. Moreover, thesis discussed advantages and capabilitied of capsule neural networks for identification of a person by its face.
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Identification of Persons in the Video from Quadcopter
Mojžiš, Tomáš ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
The aim of this thesis is to make an application capable of recognizing people's faces based on a user-created database in drone footage. The database is made of pictures of people that should be identified in the footage. The output of this application is a video where the demanded people are labeled with their names. Some face detection and recognition state of the art solutions based on neural networks are compared in this work. The final solution consists of the MTCNN detector and a face embedding extractor based on ArcFace. The created multiplatform application allows to recognize people in drone footage even with face width of less than 20 pixels. The final solution was tested on a private dataset comprised of drone footage.
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Classifiaction algorithms for face-based identification systems
Hegr, Vojtěch ; Křupka, Aleš (referee) ; Malach, Tobiáš (advisor)
The thesis deals with the research of classification algorithms for face-based identification. The aim is to implement algorithms into an existing system for face recognition and the evaluation of the impect of individual classifiers. According to the survey of face recognition methods the following classifiers were chosen for implementation: K - Nearest Neighbours (K-NN), Support Vector Machines (SVM) and the Neural Networks. These classification algorithms were implemented in C++ (Microsoft Visual Studio 2010) using the open source library OpenCV. Furthermore, the IFaVID database and the methodology used to test the implemented algorithms were introduced.
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Face Identification on Android Platform
Karhánek, Martin ; Řezníček, Ivo (referee) ; Láník, Aleš (advisor)
This work describes ways to use a person identification based on faces on mobile devices with Android platform. A reader is introduced into a structure of this system and a way to create applications for it. Besides, there are also methods usable to the face identification. Some of these methods (used in an implementation) are described in more detail. This work also contains a description of model AAM (Active Appearance Model) for implementing in mobile devices and evaluation of used algorithms results.
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From the facial reconstruction based on the skull to the identification of the individual: demands, principles, problems
Moštková, Miroslava ; Brůžek, Jaroslav (advisor) ; Blažek, Vladimír (referee)
Facial reconstruction based on the skull is a technique allowing recreation of the original facial features of an individual. However, recreation of the exact look is nearly impossible at this time. Facial reconstruction is used during archaeological research or during investigation of forensic cases. The facial reconstruction methods used are morphoscopic, morphometric, or a combination of the two. They are used during a manual and computerized process to create two dimensional or three-dimensional reconstruction of the individual. Accuracy and reliability can be determined with each one of these methods by quantitative and qualitative measurements. Reliability and accuracy of the facial reconstruction should also be considered from a view of the facial perception. The human face is perceived in a holistic-analytical way which is based on a calculation of the distances between different features. Facial reconstruction is based on the recognition of familiar faces that are perceived on the basis of internal features that are not influenced by differences in the view angle or expression. Unfamiliar faces are perceived on the basis of from external features. Faces with significant facial features are identified faster. Facial features can be organized by the significance of their influence for recognition of...
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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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Identification of Persons in the Video from Quadcopter
Mojžiš, Tomáš ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
The aim of this thesis is to make an application capable of recognizing people's faces based on a user-created database in drone footage. The database is made of pictures of people that should be identified in the footage. The output of this application is a video where the demanded people are labeled with their names. Some face detection and recognition state of the art solutions based on neural networks are compared in this work. The final solution consists of the MTCNN detector and a face embedding extractor based on ArcFace. The created multiplatform application allows to recognize people in drone footage even with face width of less than 20 pixels. The final solution was tested on a private dataset comprised of drone footage.
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