The aim of this work is to realize a system in the Matlab-Simulink environment, which will be able to detect and recognize the human face from the input image. The created model will actually simulate the biometric security systems working on the principle of face recognition. The work is divided into two parts. In the first part, several methods for face detection from image are described. We focused on the symptomatic oriented and color segmentation methods. The pattern matching method is also described and implemented; the advantage ofthe pattern matching that it can be used either for face detection or face recognition. The second part of this work contains a description of the face recognition. Where PCA (Principal Component Analysis) are used for this task, this part of the work also includes experimental results of tests performed on our methods.

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.

Condições distintas de iluminação numa imagem podem produzir representações desiguais do mesmo objeto, dificultando o processo de segmentação e reconhecimento de padrões, incluindo o reconhecimento facial. Devido a isso, a distribuição de iluminação numa imagem é considerada de grande importância, e novos algoritmos de normalização utilizando técnicas mais recentes ainda vêm sendo pesquisados. O objetivo dessa pesquisa foi o de avaliar os seguintes algoritmos de normalização da iluminação encontrados na literatura, que obtiveram bons resultado no reconhecimento de faces: LogAbout, variação do filtro homomórfico e método baseado em wavelets. O objetivo foi o de identificar o método de normalização da iluminação que resulta na melhor taxa de reconhecimento facial. Os algoritmos de reconhecimento utilizados foram: auto-faces, PCA (Principal Component Analyses) com rede neural LVQ (Learning Vector Quantization) e wavelets com rede neural MLP (Multilayer Perceptron). Como entrada, foram utilizadas imagens do banco Yale, que foram divididas em três subconjuntos. Os resultados mostraram que o método de normalização da iluminação que utiliza wavelet e LogAbout foram os que apresentaram melhoria significativa no reconhecimento facial. Os resultados também evidenciaram que, de uma maneira geral, com a utilização dos métodos de normalização da iluminação, obtém-se uma melhor taxa do reconhecimento facial, exceto para o método de normalização variação do filtro homomórfico com os algoritmos de reconhecimento facial auto-faces e wavelet com rede neural MLP. / Distinct lighting conditions in an image can produce unequal representations of the same object, compromising segmentation and pattern recognition processes, including facial recognition. Hence, the lighting distribution on an image is considered of great importance, and normalization algorithms using new techniques have still been researched. This research aims to evaluate the following illumination normalization algorithms found in literature: LogAbout, variation of homomorphic filter and wavelet based method. The main interest was to find out the illumination normalization method which improves the facial recognition rate. The algorithms used for face recognition were: eigenfaces, PCA (Principal Component Analysis) with LVQ neural network and wavelets with MLP (Multilayer Perceptron) neural network. Images from Yale Face Database B, divided into three subsets have been used. The results show that the wavelet and LogAbout technique provided the best facial recognition rate. Experiments showed that the illumination normalization methods, in general, improve the facial recognition rate, except for the variation of homomorphic filter technique with the algorithms: eigenfaces and PCA with LVQ.

Condições distintas de iluminação numa imagem podem produzir representações desiguais do mesmo objeto, dificultando o processo de segmentação e reconhecimento de padrões, incluindo o reconhecimento facial. Devido a isso, a distribuição de iluminação numa imagem é considerada de grande importância, e novos algoritmos de normalização utilizando técnicas mais recentes ainda vêm sendo pesquisados. O objetivo dessa pesquisa foi o de avaliar os seguintes algoritmos de normalização da iluminação encontrados na literatura, que obtiveram bons resultado no reconhecimento de faces: LogAbout, variação do filtro homomórfico e método baseado em wavelets. O objetivo foi o de identificar o método de normalização da iluminação que resulta na melhor taxa de reconhecimento facial. Os algoritmos de reconhecimento utilizados foram: auto-faces, PCA (Principal Component Analyses) com rede neural LVQ (Learning Vector Quantization) e wavelets com rede neural MLP (Multilayer Perceptron). Como entrada, foram utilizadas imagens do banco Yale, que foram divididas em três subconjuntos. Os resultados mostraram que o método de normalização da iluminação que utiliza wavelet e LogAbout foram os que apresentaram melhoria significativa no reconhecimento facial. Os resultados também evidenciaram que, de uma maneira geral, com a utilização dos métodos de normalização da iluminação, obtém-se uma melhor taxa do reconhecimento facial, exceto para o método de normalização variação do filtro homomórfico com os algoritmos de reconhecimento facial auto-faces e wavelet com rede neural MLP. / Distinct lighting conditions in an image can produce unequal representations of the same object, compromising segmentation and pattern recognition processes, including facial recognition. Hence, the lighting distribution on an image is considered of great importance, and normalization algorithms using new techniques have still been researched. This research aims to evaluate the following illumination normalization algorithms found in literature: LogAbout, variation of homomorphic filter and wavelet based method. The main interest was to find out the illumination normalization method which improves the facial recognition rate. The algorithms used for face recognition were: eigenfaces, PCA (Principal Component Analysis) with LVQ neural network and wavelets with MLP (Multilayer Perceptron) neural network. Images from Yale Face Database B, divided into three subsets have been used. The results show that the wavelet and LogAbout technique provided the best facial recognition rate. Experiments showed that the illumination normalization methods, in general, improve the facial recognition rate, except for the variation of homomorphic filter technique with the algorithms: eigenfaces and PCA with LVQ.

Ziel dieser Diplomarbeit ist es, eine flexibel zu verwendende Plattform für Visual Servoing-Aufgaben zu Erstellen, mit der eine Vielzahl von verschiedenen Anwendungsfällen abgedeckt werden kann. Kernaufgabe der Arbeit ist es dabei, verschiedene Verfahren der Gesichtserkennung (face detection) am Beispiel der Haar-Kaskade und -wiedererkennung (face recognition) am Beispiel von Eigenfaces und Fisherfaces zu betrachten und an ausführlichen Beispielen vorzustellen. Dabei sollen allgemeine Grundbegriffe der Bildverarbeitung und bereits bekannte Verfahren vorgestellt und ihre Implementierung im Detail dargestellt werden. Aus den dadurch gewonnen Erkenntnissen und dem sich ergebenden Anforderungsprofil an die zu entwickelnde Plattform leitet sich anschließend die Realisierung als eigenständige Anwendung ab. Hierbei ist weiterhin zu untersuchen, wie die neu zu entwickelnde Software zukunftssicher und in Hinblick auf einen möglichen Einsatz in Praktika einfach zu verwenden realisiert werden kann. Sämtliche während der Arbeit entstandenen Programme und Quellcodes werden auf einem separaten Datenträger zur Verfügung gestellt. Eine komplett funktionsfähige Entwicklungsumgebung wird als virtuelle Maschine beigelegt.

Σκοπός της παρούσας διπλωματικής εργασίας είναι η δημιουργία ενός συστήματος αναγνώρισης ταυτότητας προσώπων μέσω βιντεοσκοπήσεων. Αφού έγινε εκτενής μελέτη των τεχνικών που έχουν προταθεί για τον εντοπισμό και την αναγνώριση προσώπου επιλέχθηκαν ο αλγόριθμος Viola-Jones για το κομμάτι του εντοπισμού και η ανάλυση κυρίων συνιστωσών (PCA) για το κομμάτι της αναγνώρισης. Επίσης έγινε εφαρμογή του αλγορίθμου PCA στη βάση προσώπων ORL και μελετήθηκαν οι παράμετροι που επηρεάζουν την απόδοσή του.Τέλος, το σύστημα ταυτοποίησης που κατασκευάστηκε δοκιμάστηκε σε πραγματικές συνθήκες και προέκυψαν κάποια συμπεράσματα για την απόδοσή του. / The subject of this diploma thesis is the creation of a face recognition system from video sequences. After thoroughly studying various proposed methods for face detection and recognition, Viola-Jones algorithm and principal component analysis (PCA) algorithm were chosen for the detection and recognition parts respectively. PCA was also performed on ORL face database and its perfomance was measured. Finally the face identification system that was created, was tested on real conditions to measure its perfomance.

Baigiamajame magistro darbe yra apžvelgti metodai, naudojami veidų atpažinimui bei išanalizuotas jų veikimas. Apžvelgus veidų atpažinimo metodus buvo pasirinkti trys algoritmai (tikrinių veidų, Fišerio veidų ir 2D–DCT+SOM), kurie išsamiai išanalizuoti ir įgyvendinti MATLAB aplinkoje bei ištirti įvairus jų parametrai. Pagal gautus rezultatus buvo išrinktas optimalus algoritmas, tinkantis įgyvendinimui Android operacinėje sistemoje ir ten įgyvendintas. Baigiamajame darbe taip pat buvo apžvelgtos ir išanalizuotos problemos, su kuriomis susiduriama perkeliant algoritmą į Android operacinę sistemą, pateikti siūlymai algoritmo patobulinimui bei išvados. Visi užsibrėžti tikslai buvo pasiekti, o uždaviniai – išspręsti. Veido atpažinimo algoritmų tyrimas ir įgyvendinimas operacinėje Android sistemoje. Magistro baigiamasis darbas informatikos inžinerijos laipsniui. Vilniaus Gedimino technikos universitetas. Vilnius, 2012, 187 p., 49 iliustr., 6 lent., 74 bibl., 6 priedai. / The main goal of Master degree thesis is to review face recognition algorithms and analyze their performance. After this survey three face recognition algorithms (eigenfaces, fisherfaces and 2D–DCT+SOM) have been chosen for detailed analysis and investigation of their various parameters in MATLAB environment. According to the results obtained during this research only one algorithm, which is optimal for implementation in Android operating system, has been implemented on the mobile platform. This Master degree thesis also includes problems and suggestions regarding eigenface’s algorithm implementation in Android operating system, proposals for algorithm improvement and detailed conclusions. All the objectives have been achieved and all problems – solved. Analysis of face recognition algorithms and implementation in Android operating system. Master Thesis for Informatics Engineering degree. Vilnius Gediminas Technical University. Vilnius, 2012, 187 p., 49 figures, 6 tables, 74 references, 6 appendices.

The purpose of this study is to develop an automatic facial feature extraction system, which is able to identify the detailed shape of eyes, eyebrows and mouth from facial images. The developed system not only extracts the location information of the features, but also estimates the parameters pertaining the contours and parts of the features using parametric deformable templates approach. In order to extract facial features, deformable models for each of eye, eyebrow, and mouth are developed. The development steps of the geometry, imaging model and matching algorithms, and energy functions for each of these templates are presented in detail, along with the important implementation issues. In addition, an eigenfaces based multi-scale face detection algorithm which incorporates standard facial proportions is implemented, so that when a face is detected the rough search regions for the facial features are readily available. The developed system is tested on JAFFE (Japanese Females Facial Expression Database), Yale Faces, and ORL (Olivetti Research Laboratory) face image databases. The performance of each deformable templates, and the face detection algorithm are discussed separately.

This paper discusses problems of computer vision, which deals with face detection and recognition in image and video sequence at real time. All methods are designed for color images and are based on skin detection on the basis of information of human skin color. For skin detection is used very effective method Gaussian distribution. All of the areas, which have human skin color, are classified. This classification specifies, which area is or isn’t face. For face detection is used correlation method, complete with eigenfaces method. All areas classified as a face are subsequently recognized by the eigenfaces method. Result of recognition phase is information about human identity.

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.