A fast and accurate algorithm for facial feature segmentation

Abstrac

Аналіз та вдосконалення методу «Eigenfaces» щодо ідентифікації особистості по зображенню обличчя

Дисертація присвячена розробці автоматизованої системи розпізнавання обличь за фотографією. Оскільки існує велика кількість методів розпізнавання обличь, які відрізняються в першу чергу вартістю побудованих на їх основі алгоритмів і достовірністю розпізнавання, був проведений детальний аналіз, зазначені основні переваги та недоліки кожного з методів. На основі проведеного аналізу для реалізації був обраний метод «Eigenfaces», який дав змогу отримати високу точність розпізнавання обличь за зображеннями з невеликим відсотком помилкових спрацювань. Розроблено програму для розпізнавання обличь за зображенням мовою програмування Java у середовищі IntelliJ IDEA та наведено реалізацію алгоритму «Eigenfaces» в середовищі MATLAB. Для обох програм було розроблено зручний інтерфейс користувача. Було проведено тестування розроблених програм за різних умов. Також, розроблено стартап-проект згідно даної теми магістерської дисертації. Основні положення і висновки дисертаційної роботи доповідалися і обговорювалися на Всеукраїнській науково-технічній конференції молодих вчених у царині метрології «Technical Using of Measurement – 2018, ХХІ науково-технічній конференції студентів та молодих учених «Гіротехнології, навігація, керування рухом та конструювання авіаційно-космічної техніки», XIV Всеукраїнській науково-практичній конференції студентів, аспірантів та молодих вчених «Ефективність інженерних рішень в приладобудуванні» та ХII Науково-практичній конференції студентів, аспірантів та молодих вчених «Погляд у майбутнє приладобудування». Дисертація викладена на 92 сторінці машинописного тексту, ілюструється 31 рисунками та 24 таблицями, складається зі вступу, 6 розділів, висновків, переліку посилань з 34 найменувань.The dissertation is devoted to the development of an automated face recognition system by a photograph. Since there are a large number of face recognition methods that differ primarily in the cost of algorithms built on their basis and authenticity of recognition, a detailed analysis was carried out, the main advantages and disadvantages of each method were indicated. On the basis of the analysis, the Eigenfaces method was chosen for implementation, which made it possible to get high recognition accuracy in images with a small percentage of false positives. The program for recognition is developed for the image in the Java programming language in the IntelliJ IDEA environment and the implementation of the "Eigenfaces" algorithm in the MATLAB environment is presented. A user-friendly interface was developed for both applications. Testing developed programs under different conditions was conducted. Also, a startup project is developed in accordance with this topic of the master's thesis. The main provisions and conclusions of the dissertation work were reported and discussed at the All-Ukrainian scientific and technical conference of young scientists in the field of metrology "Technical Use of Measurement - 2018, XXI Scientific and Technical Conference of Students and Young Scientists" Girotechnology, Navigation, Motion Control and Design of Aviation and Space Equipment ", XIV All-Ukrainian scientific-practical conference of students, postgraduates and young scientists" Efficiency of engineering solutions in instrument making "and XII Scientific-practical conference of students, aspiration Ants and Young Scientists "A Look at the Future of Instrumentation." The thesis is presented on 92 page of typewritten text, illustrated with 31 figures and 24 tables, consists of an introduction, 6 sections, conclusions, list of references from 34 titles

Özyüz kullanılarak yüz tanıma

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Yüz tanıma, günümüzde önemini hızla arttırmakta olan bir uygulama konusu halinegelmektedir. Mevcut yüzlerden elde edilecek bir veri tabanı üzerinde otomatik olarakyapılacak tanımaların, suçluların teşhisi sırasında, emniyet görevlilerinin işini nekadar kolaylaştıracağı açıktır.Bu tezde, yüz tanıma problemi üzerinde araştırma yapılarak, kullanılan temelyöntemler incelendi. Yüz tanımada kullanılan yöntemlerden biri olan, özyüz(eigenface) yöntemi ile bir yüz tanıma sistemi geliştirildi.Bu tez aşağıdaki şekilde organize edildi: Birinci bölüm de yüz tanıma sisteminingetireceği kolaylıklar ele alındı. Ayrıca özyüz yönteminden kısaca bahsedildi. İkincibölüm görüntü ve yüz tanımanın temel kavramları ele alındı. Bu bölümde yüz tanımaproblemi için yapılmış olan çalışmalardan ve uygulanan metodlardan bahsedildi.Yüz tanıma problemi için iki ana yaklaşım verildi. Üçüncü bölüm önerilen yüztanıma sistemini olan özyüz tanıma metodunun detayları ele alındı. Bu bölümdeözyüzlerin hesaplanması için gerekli olan tüm işlemler anlatıldı.Dördüncü bölüm, özyüzler yaklaşımını göstermek için geliştirilmiş olan yüz tanımayazılımına ayrıldı. Yüz tanıma programı Visual C# ve Visual Basic ortakkullanılarak geliştirildi. Dördüncü bölümde geliştirilen uygulamanın nasıl kurulacağıve kullanılacağı verildi. Aynı zamanda uygulamada kullanılan önemli bir kaç rutinde verildi.In this thesis, a research was done to find out the different approaches to the facerecognition problem. It has been observed that these different approaches fall intotwo major categories that are given below:Feature based recognition, which is based on the extraction of the properties ofindividual organs located on a face such as eyes, nose and mouth, as well as theirrelationships with each other. Feature vectors describing the characteristics of faceimages are evaluated by using deformable templates and active contour models,where excessive geometry and the minimization of energy functions are involved.Principal component analysis, based on information theory concepts, seek acomputational model that best describes a face, by extracting the most relevantinformation contained in that face. Goal is to find out the eigenvectors (eigenfaces)of the covariance matrix of the distribution, spanned by a training set of face images.Later, every face image is represented by a linear combination of these eigenvectors.Evaluation of these eigenvectors are quite difficult for typical image sizes but, anapproximation that is suitable for practical purposes is also presented. Recognition isperformed by projecting a new image into the subspace spanned by the eigenfacesand then classifying the face by comparing its position in face space with thepositions of known individuals.A face recognition system, based on the eigenfaces approach is proposed. Eigenfacesapproach seems to be an adequate method to be used in face recognition due to itssimplicity, speed and learning capability

Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate

Face recognition using Hidden Markov Models

This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically

Reconhecimento de faces humanas através de técnicas de inteligência artificial aplicadas a formas 3D

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Esta tese propõe uma nova metodologia de reconhecimento de faces humanas. Diferente dos tradicionais métodos que empregam imagens bidimensionais e níveis de cinza, o paradigma aqui proposto utiliza a forma geométrica da face como parâmetro de avaliação e autenticação. Destacam-se como vantagens desta técnica o melhor desempenho principalmente frente aos problemas de iluminação e posicionamento espacial. Este trabalho engloba duas áreas de conhecimento distintas. A primeira, abordando aspectos da extração da forma tridimensional da face humana e a segunda o reconhecimento facial. A metodologia de extração da geometria baseia-se no método de Perfilometria de Fourier para obter a forma geométrica das faces. A metodologia de reconhecimento de faces divide-se em duas abordagens distintas: A primeira delas do tipo algorítmica, por Raciocínio Baseado em Casos - RBC empregando da distância de Hamming como medidor da similitude entre duas formas de faces e a segunda do tipo conexionista, baseando-se no emprego de Redes Neurais Artificiais #RNA do tipo Funções de Base Radiais - FBR, para a classificação das faces. O modelo de reconhecimento adotado nesta tese é o da verificação, onde o indivíduo se apresenta previamente e ao sistema é atribuída à tarefa de verificação da veracidade da identidade alegada. A inspiração biológica está totalmente presente neste trabalho, em primeiro lugar porque os seres humanos, no processo de reconhecimento de seus semelhantes, utilizam se das faces humanas e a forma da face é uma das grandezas identificadoras.Em segundo lugar porque as metodologias de reconhecimento inspiradas em Redes Neurais Artificiais são inerentemente paradigmas biológicos. Finalmente empregando uma grande base de faces humanas tridimensionais, são mostrados os resultados da aplicação dos paradigmas de verificação desenvolvidos, comparando-se os resultados obtidos através de uma análise detalhada

Modèle de génération de mouvements rapides en représentation de signatures manuscrites

Les systèmes de vérification de l'identité -- Les systèmes de vérification de signatures -- Représentation des signatures -- Objectifs de recherche -- Plan de thèse -- Présentation du modèle -- Justification du choix -- Historique du modèle -- Etude des courbes delta lognormales -- Courbes lognomrales -- Courbes delta lognormales -- Représentation des signatures -- Synthèse de tracé manuscrits -- Extraction de la statique -- Extraction de la cinématique -- Enoncé de la représentation -- Avantages de la représentation des signatures -- Variation de la prcision -- Précision constante -- Validation de la représentation des signatures -- Description de la banque de signatures -- Description des traitements -- Extraction des paramètres -- Protocole de la validation de la repésentation -- Résultats de la validation de la représentation -- Résultats de la validation de la représentation

Pose-invariant face recognition using real and virtual views

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 173-184).by David James Beymer.Ph.D