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Face recognition in digital images
Hauser, Václav ; Přinosil, Jiří (referee) ; Říha, Kamil (advisor)
This master thesis deals with the detection and recognition of faces in the image. The content of this thesis is a description of methods that are used for the face detection and recognition. Method described in detail is the principal component analysis (PCA). This method is subsequently used in the implementation of face recognition in video sequence. In conjunction with the implementation work describes the OpenCV library package, which was used for implementation, specifically the C ++ API. Finally described application tests were done on two different video sequences.
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Social Network from Photo Gallery
Polesný, Ondřej ; Přibyl, Bronislav (referee) ; Mlích, Jozef (advisor)
This thesis deals with recognition of persons and their relationships from static images and presents ways in which these information can be used. That includes methods for detection and recognition of faces, presentation of photos in web-based gallery and methods for recognition of relationships between photographed persons. Part of this thesis is evaluation of the results, both in terms of program success in automated operations by establishing a set of test data with known relationships between people, and in terms of efficiency and functionality of the system for end users.
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Biometric 2D face recognition from camera system placed on a quadrocopter
Mikundová, Lea ; Mráček, Štěpán (referee) ; Drahanský, Martin (advisor)
This Bc. thesis is devoted to face recognization from camera system placed on a quadrocopter. The teoretical part is about the most used methods for detection and face recognition and their comparison. The next part is about motion capturing from quadrocopter. Practical part of thesis is devoted to implementation of algorithms for face detection and recognization by OpenCV library and evaluation of algorithm in respect to distance and angle of quadrocopter due to captured person.
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Detection and Recognition of Dominant Face Features
Švábek, Hynek ; Láník, Aleš (referee) ; Chmelař, Petr (advisor)
This thesis deals with the increasingly developing field of biometric systems which is the identification of faces. The thesis deals with the possibilities of face localization in pictures and their normalization, which is necessary due to external influences and the influence of different scanning techniques. It describes various techniques of localization of dominant features of the face such as eyes, mouth or nose. Not least, it describes different approaches to the identification of faces. Furthermore a it deals with an implementation of the Dominant Face Features Recognition application, which demonstrates chosen methods for localization of the dominant features (Hough Transform for Circles, localization of mouth using the location of the eyes) and for identification of a face (Linear Discriminant Analysis, Kernel Discriminant Analysis). The last part of the thesis contains a summary of achieved results and a discussion.
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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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Methods and algorithms for face recognition
Soukup, Jiří ; Heriban, Pavel (referee) ; Šťastný, Jiří (advisor)
This work is describing basic methods of face recognition. The methods PCA, LDA, ICA, trace tranfsorm, elastic bunch graph map, genetic algorithm and neural network are described. In practical part, the PCA, PCA + RBF neural network and genetic algorithms are implemented. The RBF neural network is used in the way of clasificator and genetic algorithm is used for RBF NN training in one case and for selecting eigenvectors from PCA method in the other case. This method, PCA + GA, called EPCA, outperform other methods tested in this work on the ORL testing database.
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Application for Recognition of People by Face
Svoboda, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Person identification has in the recent years gained notoriety as one of the most powerful ways of extracting information from image data. This thesis is focused on the task of human identification from facial photographs. To solve this task, we employ algorithms based on neural networks, which produce more robust results than traditional algorithms. In this thesis, we studied the common approaches for solving this problem and based on the gathered knowledge we created an architecture of a neural network trained to tackle the task of human identification and verification based on facial photographs. We have then further improved the model architecture and the training process by performing various experiments and observing the results. The final model has reached an accuracy comparable to other state-of-the-art models. Furthermore, we created a desktop application to demonstrate the results visually and to enable easier manipulation with the identity database. The knowledge gathered in this thesis can be used for improvements of current identification models or models modified for solving similar tasks.
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Face Recognition
Kopřiva, Adam ; Hradiš, Michal (referee) ; Chmelař, Petr (advisor)
This master's thesis considers methods of face recognition. There are described methods with different approachs: knowledge-based methods, feature invariant approaches, template matching methods and appearance-based methods. This master's thesis is focused particulary on template matching method and statistical methods like a principal component analysis (PCA) and linear discriminant analysis (LDA). There are described in detail template matching methods like active shape models (ASM) and active appearance models (AAM).
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Biometrics using Face Recognition
Koupil, Michal ; Mézl, Martin (referee) ; Odstrčilík, Jan (advisor)
This work is focused on a face detection in a picture and subsequent recognition of the face in its respective database. Face detection had been implemented using Viola-Jones algorithm. To recognize the face afterwards, PCA (Principal Component Analysis) a LBPH (Local Binary Pattern Histograms) had been used. Implemented algorithm had been tested on freely accesible biometric databases. Overall success rate of face detection was 93,4 %. Overall success rate of face recognition using PCA was 88,1 % and with LBPH it was 93,1 %. Of the two methods for face recognition, LBPH method has better ability to perform recognition with data, that do not possess ideal parameters for biometrics. With data suitable for biometrics, both of the methods perform well, with PCA being faster.
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Face Recognition
Keršner, Martin ; Mlích, Jozef (referee) ; Juránek, Roman (advisor)
The thesis deals with Face Recognition. The aim was to study the various methods of feature extraction and determine their influence on the success of recognition. The methods of feature extraction include the Local Binary Pattern, Histogram Of Oriented Gradients and Gabor Filter. Face recognition of image similarity will be described. Support Vectore Machines was used in the experiments. Experimentally determined parameters of the most successful methods were used in the system for simple Face Recognition.
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