Анализ преобразований для проецирования данных на обобщенную ось в задачах распознавания образов

Решена задача разработки и исследования преобразований из двумерного пространства в одномерное для сокращения размерности обучающей выборки в задачах распознавания образов. Предложены рекомендации по использованию исследованных преобразований, позволяющие их ранжировать и сократить при использовании на практике.Вирішено завдання створення та дослідження перетворень із двовимірного простору в одновимірний для скорочення розмірності навчальної вибірки в задачах розпізнавання образів. Запропоновано рекомендації з використання досліджених перетворень, що дозволяють їх ранжирувати і скоротити при використанні на практиці.The problem of development and analysis of transformations from 2D to 1D space is solved with the aim to reduce the training set dimension in pattern recognition problems. The recommendations on the use of the investigated transformations are proposed, which allow to range and reduce them using in action

A Real-time Model for Multiple Human Face Tracking from Low-resolution Surveillance Videos

AbstractThis article discusses a novel approach of multiple-face tracking from low-resolution surveillance videos. There has been significant research in the field of face detection using neural-network based training. Neural network based face detection methods are highly accurate, albeit computationally intensive. Hence neural network based approaches are not suitable for real-time applications. The proposed approach approximately detects faces in an image solely using the color information. It detects skin region in an image and finds existence of eye and mouth region in the skin region. If it finds so, it marks the skin region as a face and fits an oriented rectangle to the face. The approach requires low computation and hence can be applied on subsequent frames from a video. The proposed approach is tested on FERET face database images, on different images containing multiple faces captured in unconstrained environments, and on frames extracted from IP surveillance camera

Human Face Detection Technique Based-on Modified Adaptive Resonance Theory Network

ได้รับทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนารี ปีงบประมาณ พ.ศ.255

Statistical facial feature extraction and lip segmentation

Facial features such as lip corners, eye corners and nose tip are critical points in a human face. Robust extraction of such facial feature locations is an important problem which is used in a wide range of applications including audio-visual speech recognition, human-computer interaction, emotion recognition, fatigue detection and gesture recognition. In this thesis, we develop a probabilistic method for facial feature extraction. This technique is able to automatically learn location and texture information of facial features from a training set. Facial feature locations are extracted from face regions using joint distributions of locations and textures represented with mixtures of Gaussians. This formulation results in a maximum likelihood (ML) optimization problem which can be solved using either a gradient ascent or Newton type algorithm. Extracted lip corner locations are then used to initialize a lip segmentation algorithm to extract the lip contours. We develop a level-set based method that utilizes adaptive color distributions and shape priors for lip segmentation. More precisely, an implicit curve representation which learns the color information of lip and non-lip points from a training set is employed. The model can adapt itself to the image of interest using a coarse elliptical region. Extracted lip contour provides detailed information about the lip shape. Both methods are tested using different databases for facial feature extraction and lip segmentation. It is shown that the proposed methods achieve better results compared to conventional methods. Our facial feature extraction method outperforms the active appearance models in terms of pixel errors, while our lip segmentation method outperforms region based level-set curve evolutions in terms of precision and recall results

Detecção facial: autofaces versus antifaces

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.No presente trabalho, é desenvolvido um estudo comparativo entre duas técnicas de detecção facial baseadas em projeções vetoriais: Autofaces e Antifaces. O método de Autofaces tem sido significativamente estudado nos últimos anos, enquanto o de Antifaces é ainda considerado o estado-da-arte para a detecção de objetos. Ambos os métodos são descritos de forma detalhada e, para o método de Antifaces, é proposto um procedimento que permite obter os detectores subótimos. Ambos os métodos são avaliados em condições idênticas de teste. Tais avaliações consideram detecções de características faciais, de objetos tridimensionais e de uma face específica, vista de um ângulo frontal. Finalmente, é feita uma análise de sensibilidade dos métodos ao ruído branco Gaussiano aditivo, a distorções no foco e a alterações na cena em que se apresenta o objeto de interesse. Através dos resultados obtidos, é possível constatar que, no método de Antifaces, os critérios para a determinação de algumas variáveis de projeto não estão ainda bem estabelecidos. Além disso, esse método apresenta alta seletividade durante o processo de detecção. O método de Autofaces possui maior capacidade de generalização e menor sensibilidade à adição de ruído, distorções no foco e alterações na cena

Human detection and face recognition in indoor environment to improve human-robot interaction in assistive and collaborative robots

Human detection in indoor environment is essential for Robots working together with humans in collaborative manufacturing environment. Similarly, Human detection is essential for service robots providing service with household chores or helping elderly population with different daily activities. Human detection can be achieved by Human Head detection, as head is the most discriminative part of human. Head detection method can be divided into three types: i) Method based on color mode; ii) Method based on template matching; and iii) Method based on contour detection. Method based on color mode is simple but is error prone. Method based on head template detects head in the image by searching for a template which is similar to head template. On the other hand, Method based on contour detection uses some information to describe head or head and shoulder information. The use of only one criteria may not be sufficient and accuracy of human head detection can be increased by combining the shape and color information. In this thesis, a method of human detection is proposed by combining the head shape and skin color (i.e., Combination of method based on Color mode and method based on Contour detection). Mainly, curvature criteria is used to segment out curves having similar curvature to find human head. Further, skin color is detected to localize face in image plane. A curve represents human head curve if only it has sufficient skin colored pixel in its closed proximity. Thus, by using color and human head curvature it was found that promising results could be obtained in human detection in indoor environment. iv After detecting humans in the surrounding, the next step for the robot could be to identify and recognize them. In this thesis, the use of Gabor filter response on nine points was investigated to identify eight different individuals. This suggests that the Gabor filter on nine points could be applied to identify people in small areas, for example home or small office with less individuals.Masters of Applied Science (M.A.Sc.) in Natural Resource Engineerin

Face Pose Estimation using a Tree of Boosted Classifiers

Face detection in images or video sequences is a very challenging problem. It has a wide range of applications but at the same time it presents a great number of difficulties, since faces are non-rigid and very changeable objects that can adopt a lot of different poses and with a high inter and intra-person variation and a high sensitivity to lighting conditions. Along this document, a new approach to the face detection and pose estimation problem is given. This approach is based on the method proposed by Viola and Jones in [1] but considering a wide range of face poses, varying the elevation and the out-of-plane rotation, and building specific classifiers for each one. The proposed method can be easily adapted to consider other poses or to detect other objects. Especially, this approach is interesting when an object that can adopt several positions want to be detected, since the partition of the pose space allows to build classifiers specialised in only one or a few poses, which limits the large variance of the global class, the class containing all the poses. In order to facilitate the reproduction of all the processes done in this document, we have used standard face datasets to train and test the system

Iskanje obrazov na osnovi barv s pomočjo statističnih metod razpoznavanja vzorcev

V zadnjem času postaja video nadzor vse pomembnejši in s tem tudi sistemi za iskanje in prepoznavo človeških obrazov na slikah. Zato se v magistrskem delu ukvarjam s problemom iskanja obrazov na slikah. Pri metodah za iskanje obrazov na podlagi barve smo velikokrat omejeni na človeške obraze samo določene polti, same metode pa so tudi zelo odvisne od osvetlitve. V magistrskem delu zato poskušam s pomočjo kromatičnega barvnega prostora odvisnost od osvetlitve zmanjšati. Preizkusil bom različne metode za barvno segmentacijo na osnovi parametričnega in neparametričnega modela. S pomočjo teh modelov bom poskušal modelirati kožno barvo pri različnih osvetlitvah in različnih kožnih polteh. Uspešnost metod bom primerjal z metodo, ki deluje v barvnem prostoru RGB na osnovi eksplicitno določenih mej. Za potrjevanje označenih kožnih regij bom uporabil metodo na osnovi videza, ki nam med vsemi metodami obljublja najboljše rezultate. Izdelal in preizkusil bom metodo BDF, ki na osnovi naučenega vzorca obraza in neobraza s pomočjo Bayesovega klasifikatorja najde frontalne obraze na sivinskih slikah. Glavna slabost metod na osnovi videza je njihova časovna zahtevnost, zato bom poskušal izdelati metodo, ki bo kombinirala pristop na osnovi barv in pristop na osnovi videza. S pomočjo tako izdelane metode bom poskušal doseči hitro in učinkovito iskanje frontalnih obrazov na barvnih slikah

Information theoretic combination of classifiers with application to face detection

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empirical experience proved that is is often much easier finding several relatively good classifiers than only finding one single very accurate predictor. The advantages of combining classifiers instead of single classifier schemes are twofold: it helps reducing the computational requirements by using simpler models, and it can improve the classification skills. It is commonly admitted that classifiers need to be complementary in order to improve their performances by aggregation. This complementarity is usually termed as diversity in classifier combination community. Although diversity is a very intuitive concept, explicitly using diversity measures for creating classifier ensembles is not as successful as expected. In this thesis, we propose an information theoretic framework for combining classifiers. In particular, we prove by means of information theoretic tools that diversity between classifiers is not sufficient to guarantee optimal classifier combination. In fact, we show that diversity and accuracies of the individual classifiers are generally contradictory: two very accurate classifiers cannot be diverse, and inversely, two very diverse classifiers will necessarily have poor classification skills. In order to tackle this contradiction, we propose a information theoretic score (ITS) that fixes a trade-off between these two quantities. A first possible application is to consider this new score as a selection criterion for extracting a good ensemble in a predefined pool of classifiers. We also propose an ensemble creation technique based on AdaBoost, by taking into account the information theoretic score for iteratively selecting the classifiers. As an illustration of efficient classifier combination technique, we propose several algorithms for building ensembles of Support Vector Machines (SVM). Support Vector Machines are one of the most popular discriminative approaches of pattern recognition and are often considered as state-of-the-art in binary classification. However these classifiers present one severe drawback when facing a very large number of training examples: they become computationally expensive to train. This problem can be addressed by decomposing the learning into several classification tasks with lower computational requirements. We propose to train several parallel SVM on subsets of the complete training set. We develop several algorithms for designing efficient ensembles of SVM by taking into account our information theoretic score. The second part of this thesis concentrates on human face detection, which appears to be a very challenging binary pattern recognition task. In this work, we focus on two main aspects: feature extraction and how to apply classifier combination techniques to face detection systems. We introduce new geometrical filters called anisotropic Gaussian filters, that are very efficient to model face appearance. Finally we propose a parallel mixture of boosted classifier for reducing the false positive rate and decreasing the training time, while keeping the testing time unchanged. The complete face detection system is evaluated on several datasets, showing that it compares favorably to state-of-the-art techniques