Tato diplomová práce se zabývá využitím neuronových sítí pro detekci obličeje v obraze. Práce poskytuje základní informace nezbytné pro pochopení detekce obličejů a neuronových sítí. Dále se věnuje současným nejúspěšnějším detektorům, především detektorům založeným na neuronových sítích. Detailně je pak popsán detektor, který navrhl Rowley. Z tohoto detektoru moje práce ve velké míře čerpá. Dále je popsána implementace tohoto detektoru společně s navrženými zlepšeními a jsou prezentovány výsledky provedených testů.

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.

This work deals with image processing and face detection. Includes approaches to the problems of image processing. Furthermore, it focuses mainly on the choice of appropriate libraries and implementation of algorithms able to detect faces from the input image data.

The aim of this master thesis is to understand problematic of eyes movement tracking using webcam. In individual chapters there are introduced basic approaches of eyes movement tracking, based on this method we can get the position of eye pupil and identify where a person looks. The part of this work is program in Matlab, which using the method of infrared detection follow the position of eye pupil and transfer this movements to the movements on the computer screen. Using this program it was tested several volunteers and the results of detection were transferred to the visual form. As part of this program it was created template for assessment of accuracy of used method. At the end of work are discussed achieved results.

This thesis is about face detection and recognition from video. Main emphasis is on computational speed, so it can be used for a real-time processing. Begining of this work focus on different approaches for detection and object recognition. Afterwards is explained the main principle of methods used for the final application. Next part is about design and implementation of this methods and conclusion is about the testing results of designed application.

This work deals with task of face detection on graphic card. First part is the introduction to face detection methods focusing on detector proposed by Viola and Jones. Further, this work studies the possibilities of mapping detector's key parts on graphic card. Next part describes implementation details of designed application. The end of work include results and comparison with CPU approach. The last chapter summarizes the whole work and proposes future possibilities of development.

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.

This diploma thesis deals with face recognition in digital pictures. The first part describes biometry and, shortly, characterizes biometrical methods which are the most oftenly used. In the second part is described the approach of face recognition in a picture. Specifically, it is described the method for face detection - Viola-Jones and method for face recognition - PCA, which will be implemented in Matlab. The last part, which is practical, describes the scheme for video-sequence recording, implementation of the PCA method in Matlab and discussion of the achieved results.

<div>Face redaction is used to deidentify images of people. Most approaches depend on face detection, but automated algorithms are still not adequate for sensitive applications in which even one unredacted face could lead to irreversible harm. Human annotators can potentially provide the most accurate detection, but only trusted annotators should be allowed to see the faces of privacy-sensitive applications. Redacting more images than trusted annotators could accommodate requires a new approach. </div><div>This dissertation leverages the characteristics of human perception of faces in median-filtered images in a human computation algorithm to engage crowd workers to redact faces—without revealing the identities. IntoFocus, a system I developed, permits robust face redaction with probabilistic privacy guarantees. The system's design builds on an experiment that measured the filter levels and conditions where participants could detect and identify faces. </div><div> Pterodactyl is a system that focuses on increasing the productivity of crowd-based face redaction systems. It uses the AdaptiveFocus filter, a filter that combines human perception of faces in median filtered images with a convolutional neural network to estimate a median filter level for each region of the image to allow the faces to be detected and prevent them from being identified.</div>

With the rapid worldwide spread of Coronavirus (COVID-19 and COVID-20), wearing face masks in public becomes a necessity to mitigate the transmission of this or other pandemics. However, with the lack of on-ground automated prevention measures, depending on humans to enforce face mask-wearing policies in universities and other organizational buildings, is a very costly and time-consuming measure. Without addressing this challenge, mitigating highly airborne transmittable diseases will be impractical, and the time to react will continue to increase. Considering the high personnel traffic in buildings and the effectiveness of countermeasures, that is, detecting and offering unmasked personnel with surgical masks, our aim in this paper is to develop automated detection of unmasked personnel in public spaces in order to respond by providing a surgical mask to them to promptly remedy the situation. Our approach consists of three key components. The first component utilizes a deep learning architecture that integrates deep residual learning (ResNet-50) with Feature Pyramid Network (FPN) to detect the existence of human subjects in the videos (or video feed). The second component utilizes Multi-Task Convolutional Neural Networks (MT-CNN) to detect and extract human faces from these videos. For the third component, we construct and train a convolutional neural network classifier to detect masked and unmasked human subjects. Our techniques were implemented in a mobile robot, Thor, and evaluated using a dataset of videos collected by the robot from public spaces of an educational institute in the U.S. Our evaluation results show that Thor is very accurate achieving an F_{1} score of 87.7% with a recall of 99.2% in a variety of situations, a reasonable accuracy given the challenging dataset and the problem domain.