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Application of Neural Networks for Human Face Localization
Libosvár, Jakub ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
This bachelor thesis deals with detection and localization of human upright faces in images. At first, there are considered current methods. Then the face detection concept is presented. The thesis is focused on practical implementation of artificial neural network-based face detector designed by H. Rowley. Finally, training process and results of detector are discussed in more detail.
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Gaze Detection
Caha, Miloš ; Herout, Adam (referee) ; Španěl, Michal (advisor)
Main object of this work is to design and implement the algorithm for look direction determination, respectively the head movement. More specifically, it is a system that searches face in the video and then detects points, suitable for view direction estimation of tracked person. Estimation is realized using searching transformation, which has been performed on key points during head movement. For accuracy enhancement the calibration frames are used. Calibration frames determines the key points transformation in defined view directions. Main result is an application able to determine deflection of head from straight position in horizontal and vertical direction for tracked person. Output doesn't contain only information about deflection direction, but it also contains the size of deflection.
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Semiautomatic Collection of Large Database of Human Faces
Michalík, Marek ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
The project is focused on methods of obtaining large number of images of human faces. Such database should then serve as a set of data for face detection and recognition by the means of supervised machine learning. The work deals with the basic principles of supervised machine learning and available data sets for this procedure. Project contains proposals of techniques and implementation of algorithms suitable for acquiring images from video and a concept of user interface for semi-automatic acceptation and annotation of located images.
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Human-Machine Interface Based on Gestures
Charvát, Jaroslav ; Beran, Vítězslav (referee) ; Bartoň, Radek (advisor)
Master's thesis "Human-Machine Interface Based on Gestures" depicts the theoretical background of the computer vision and gesture recognition. It describes more in detail different methods that were used to create the application. Practical part of this thesis consists of the description of the developed program and its functionality. Using this application, user should be able to control computer by gestures of both right and left hands and also his head. The program is primarily based on the skin detection that is followed by the recognition of palms and head gestures. There were used two essential methods for these actions, AdaBoost and PCA.
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AdaBoost in Computer Vision
Hradiš, Michal ; Zemčík, Pavel (referee) ; Potúček, Igor (advisor)
In this thesis, we present the local rank differences (LRD). These novel image features are invariant to lighting changes and are suitable for object detection in programmable hardware, such as FPGA. The performance of AdaBoost classifiers with the LRD was tested on a face detection dataset with results which are similar to the Haar-like features which are the state of the art in real-time object detection. These results together with the fact that the LRD are evaluated much faster in FPGA then the Haar-like features are very encouraging and suggest that the LRD may be a solution for future hardware object detectors. We also present a framework for experiments with boosting methods in computer vision. This framework is very flexible and, at the same time, offers high learning performance and a possibility for future parallelization. The framework is available as open source software and we hope that it will simplify work for other researchers.
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Face Detection
Šašinka, Ondřej ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
This MSc Thesis deals with face detection in image. In this approach, facial features (eyes, nose, mouth corners) are detected first and then joined to the whole face. For the facial features detection, classifiers trained with AdaBoost algorithm are used. Haar wavelets are used as features for classification.
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Video-Based Human-Computer Interface
Caha, Miloš ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
A bachelor thesis deals with methods about a detection of direction of look and with theirs following applications for PCs control. The thesis summarises and describes the most widely used methods for individual phases of the mentioned detection. Especially, use of a face detector and of convolution filters for searching of significant points in the face is used. The work is concentrated on a design and a description of an implementation of the application which demonstrates the method of contactless PCs control.
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Face Detection in Camera Image on a Mobile Phone
Tureček, Martin ; Láník, Aleš (referee) ; Herout, Adam (advisor)
This thesis deals with a face detection on mobile phones. It especially focuses on Windows Mobile platform. The introduction is therefore devoted to this operating system and alternatives of working with the camera. The next part of the text refers to general problems of the face detection in the image considering the weak performance of the target device. Another part of this thesis is a description of the acquisition of images from the camera using DirectShow multimedia framework and creation of a custom transformation filter for the face detection. Achieved results are summarized in the conclusion. It takes a form of tests examining different mobile devices. All difficulties arising during Windows Mobile developing are also mentioned.
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Detection of facial expression
Kozák, Roman ; Krček, Petr (referee) ; Roupec, Jan (advisor)
This master‘s thesis deals with the design and implementation of computer application for facial expression recognition. The first section describes the image processing, especially algorithms used in other parts of this thesis. The next section describes methods used for face detection, particularly the Viola-Jones detector. This is followed by a brief description of the basic facial expressions recognized by the application. The last two sections are dedicated to the design and implementation of the facial expression recognition algorithms and to the evaluation of the results.
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