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Face detection in image
Pospíšil, Lukáš ; Harabiš, Vratislav (referee) ; Mézl, Martin (advisor)
The main puprose of this paper is to introduce some of algorithms used in face detection in images. This paper focuses on face detection by using a skin-color based segmentation that is computed in three color spectrums (RGB,YCbCr and HSV). This paper also includes software for face detection in pictures and videos.
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Advanced Detection of Human Face
Koníček, Igor ; Juránek, Roman (referee) ; Herout, Adam (advisor)
With detection, localization and pose estimation of face was used to deal as three different tasks. This thesis works with algorithms which unify this tasks into one algorithm. To solve this problem histograms of oriented gradients connected in tree based structure are used. First part of work briefly informs about methods used for face detection. Aim of this work is to experiment with given algorithms and discover their quality for face detection and glasses detection.
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Neural Network Based Face Localization
Hendrych, Pavel ; Šiler, Ondřej (referee) ; Švub, Miroslav (advisor)
This thesis issues with possible methods for face detection and localization according to the state of the art. It describes various approaches and it is aimed at localization by neural networks and at necessary operations that have to be done before localization and after that for correct results representation. This project contains implementation of few approaches to neural netwok based face localization with emphasis on eigenfaces based face localization as well as implementation of simple classifier using distance of reconstructed face to the original one. Detailed description of implemented system, achieved results and dependecy of system performance on it's inner settings is also provided.
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Object Detection in Images
Vaľko, Tomáš ; Motlíček, Petr (referee) ; Švub, Miroslav (advisor)
Object detection in images is quite popular topic for years. What stands for it are a lot of works from this area of computer science. This thesis is about object classification, specifically human faces, which are one of the most interesting objects for processing. For classification we use neural networks, learned on face database. We study what influence has size of face database and preprocessing of digital image on neural network learning. This project implements simple face detector and localizator. It summarizes more and less successful results and indicates possible ways of system development in the future.
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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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Biometrics using face recognition
Koupil, M. ; Odstrčilík, J.
This paper 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 had been used. Implemented algorithm had been tested on freely accessible biometric databases.
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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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Neural Networks for Human Face Detection in Images
Henzl, Martin ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
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.
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Face detection in image
Pospíšil, Lukáš ; Harabiš, Vratislav (referee) ; Mézl, Martin (advisor)
The main puprose of this paper is to introduce some of algorithms used in face detection in images. This paper focuses on face detection by using a skin-color based segmentation that is computed in three color spectrums (RGB,YCbCr and HSV). This paper also includes software for face detection in pictures and videos.
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