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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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Application of Neural Networks for Human Face Localization
Žák, Jakub ; Štancl, Vít (referee) ; Švub, Miroslav (advisor)
This paper describes aplication of multi layered neural network for solving problem of detection human face in static picture. This Method has good generalizational capabilities in general and there is no need to assembly complex models of analyzed data. There is also mentioned posibility of using neural network with changed architecture in this work.
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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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Development of algorithms for digital real time image processing on a DSP Processor
Knapo, Peter ; Sajdl, Ondřej (referee) ; Belgium, Jurgen Baert (MSc), KHBO (advisor)
Rozpoznávanie tvárí je komplexný proces, ktorého hlavným ciežom je rozpoznanie žudskej tváre v obrázku alebo vo video sekvencii. Najčastejšími aplikáciami sú sledovacie a identifikačné systémy. Taktiež je rozpoznávanie tvárí dôležité vo výskume počítačového videnia a umelej inteligencií. Systémy rozpoznávania tvárí sú často založené na analýze obrazu alebo na neurónových sieťach. Táto práca sa zaoberá implementáciou algoritmu založeného na takzvaných „Eigenfaces“ tvárach. „Eigenfaces“ tváre sú výsledkom Analýzy hlavných komponent (Principal Component Analysis - PCA), ktorá extrahuje najdôležitejšie tvárové črty z originálneho obrázku. Táto metóda je založená na riešení lineárnej maticovej rovnice, kde zo známej kovariančnej matice sa počítajú takzvané „eigenvalues“ a „eigenvectors“, v preklade vlastné hodnoty a vlastné vektory. Tvár, ktorá má byť rozpoznaná, sa premietne do takzvaného „eigenspace“ (priestor vlastných hodnôt). Vlastné rozpoznanie je na základe porovnania takýchto tvárí s existujúcou databázou tvárí, ktorá je premietnutá do rovnakého „eigenspace“. Pred procesom rozpoznávania tvárí, musí byť tvár lokalizovaná v obrázku a upravená (normalizácia, kompenzácia svetelných podmienok a odstránenie šumu). Existuje mnoho algoritmov na lokalizáciu tváre, ale v tejto práci je použitý algoritmus lokalizácie tváre na základe farby žudskej pokožky, ktorý je rýchly a postačujúci pre túto aplikáciu. Algoritmy rozpoznávania tváre a lokalizácie tváre sú implementované do DSP procesoru Blackfin ADSP-BF561 od Analog Devices.
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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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Application of Neural Networks for Human Face Localization
Žák, Jakub ; Štancl, Vít (referee) ; Švub, Miroslav (advisor)
This paper describes aplication of multi layered neural network for solving problem of detection human face in static picture. This Method has good generalizational capabilities in general and there is no need to assembly complex models of analyzed data. There is also mentioned posibility of using neural network with changed architecture in this work.
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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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Facial Expression Recognition
Král, Jiří ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
Many views to facial expression recognition exist. This work presents one of approaches. Existing methods of human face representation by model are discussed. The AAM method, where final appearance model is created from model of shape and model of texture is proposed. Model of shape and model of texture is created by statistic analysis. Using this representation, an effective method is achieved that is complexity of information for searched face in static image. Choice and combination of suitable features for classification of facial expression is principle for facial expression recognition based on AAM. Two approaches of facial expression classification are compared. Classification based on LDA and classification based on SVM. These methods with necessary face localization using AdaBoost form an automated face recognizer in image.
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