Avatars are used more and more in online communication, in both games and socialmedia. At the same time technology for facial puppetry, where expressions of the user aretransferred to the avatar, has developed rapidly. Why is it that facial puppetry, despite this,is conspicuous by its absence? This thesis analyzes the available and upcoming solutions for facial puppetry, if a com-mon framework or library can exist and what can be done to simplify the process for de-velopers who wants to implement facial puppetry. A survey was conducted to get a better understanding of the technology. It showedthat there is no standard yet for how to describe facial expressions, but part of the marketis converging towards a common format. It also showed that there is no existing inter-face that can handle communication with tracking devices or translation between differentexpression formats. Several prototypes for recording and streaming facial expression data from differentsources were implemented as a practical test. This was done to evaluate the complexity ofimplementing real-time facial puppetry. It showed that it is not always possible to integratethe available tracking solutions into an existing project. When integration was possible itrequired a lot of work. The best way to get tracking right now seems to be to implement astandalone program for tracking that streams the tracked data to the main application. In summary it is the poor integrability of the solutions that makes it problematic forthe developers, together with a wide variety of facial expression formats. A software thatacts like a bridge between the tracking solutions and the game could allow for translationbetween different formats and simplify implementation of support. In the future, instead of working towards making all tracking solutions output stan-dardized tracking data, research further how to build a framework that can handle differ-ent configurations. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Etude bibliographique sur le recalage d'images de visage et sur le recalage d'images et travail en collaboration avec Son VuS, pour définir la précision nécessaire du recalage en fonction des exigences des méthodes de reconnaissance de visages. / Face alignment is an important step in a typical automatic face recognition system.This thesis addresses the alignment of faces for face recognition applicationin video surveillance context. The main challenging factors of this research includethe low quality of images (e.g., low resolution, motion blur, and noise), uncontrolledillumination conditions, pose variations, expression changes, and occlusions. In orderto deal with these problems, we propose several face alignment methods using differentstrategies. The _rst part of our work is a three-stage method for facial pointlocalization which can be used for correcting mis-alignment errors. While existingalgorithms mostly rely on a priori knowledge of facial structure and on a trainingphase, our approach works in an online mode without requirements of pre-de_nedconstraints on feature distributions. The proposed method works well on images underexpression and lighting variations. The key contributions of this thesis are aboutjoint image alignment algorithms where a set of images is simultaneously alignedwithout a biased template selection. We respectively propose two unsupervised jointalignment algorithms : \Lucas-Kanade entropy congealing" (LKC) and \gradient correlationcongealing" (GCC). In LKC, an image ensemble is aligned by minimizing asum-of-entropy function de_ned over all images. GCC uses gradient correlation coef-_cient as similarity measure. The proposed algorithms perform well on images underdi_erent conditions. To further improve the robustness to mis-alignments and thecomputational speed, we apply a multi-resolution framework to joint face alignmentalgorithms. Moreover, our work is not limited in the face alignment stage. Since facealignment and face acquisition are interrelated, we develop an adaptive appearanceface tracking method with alignment feedbacks. This closed-loop framework showsits robustness to large variations in target's state, and it signi_cantly decreases themis-alignment errors in tracked faces.

The thesis deals with automatic control the font size by the distance from the reader. It includes theoretical acquaintance with the face detection and subsequent tracking of the detected area during the scene. Furthermore, there is a comparison of the tracking algorithms. Then the calculation of distance is decribed. It is based on the user’s calibration and based on the outcome occurs the font size is automatically corrected. There is also a description of a separate application Automatical controller of the text size, with the recommended settings of the program.

The project is focused on face detection in video. Firstly, it contains a summary of basic color models. Secondly, you can find the description and comparison of the basic methods for detection of human skin with a practical example of implementation of parametric detector. Thirdly, a theoretical basis for face detection and face tracking in a video containing a list of basic concepts and methods of this issue follows. Greater emphasis is placed on the description of machine learning algorithm AdaBoost and description of the possible application of the Kalman filter for the purpose of face tracking. Design, implementation and testing of library accomplished within the master thesis are listed in the final part of this thesis.

Une vidéo est une source particulièrement riche en informations. Parmi tous les objets que nous pouvons y trouver, les visages humains sont assurément les plus saillants, ceux qui attirent le plus l’attention des spectateurs. Considérons une séquence vidéo dont chaque trame contient un ou plusieurs visages en mouvement. Ils peuvent appartenir à des personnes connues ou qui apparaissent de manière récurrente dans la vidéo Cette thèse a pour but de créer une méthodologie afin d’extraire une ou plusieurs images de visage en vue d’appliquer, par la suite, un algorithme de reconnaissance du visage. La principale hypothèse de cette thèse réside dans le fait que certains exemplaires d’un visage sont meilleurs que d’autres en vue de sa reconnaissance. Un visage est un objet 3D non rigide projeté sur un plan pour obtenir une image. Ainsi, en fonction de la position relative de l’objectif par rapport au visage, l’apparence de ce dernier change. Considérant les études sur la reconnaissance de visages, on peut supposer que les exemplaires d’un visage, les mieux reconnus sont ceux de face. Afin d’extraire les exemplaires les plus frontaux possibles, nous devons d’une part estimer la pose de ce visage. D’autre part, il est essentiel de pouvoir suivre le visage tout au long de la séquence. Faute de quoi, extraire des exemplaires représentatifs d’un visage perd tout son sens. Les travaux de cette thèse présentent trois parties majeures. Dans un premier temps, lorsqu’un visage est détecté dans une séquence, nous cherchons à extraire position et taille des yeux, du nez et de la bouche. Notre approche se base sur la création de cartes d’énergie locale principalement à direction horizontale. Dans un second temps, nous estimons la pose du visage en utilisant notamment les positions relatives des éléments que nous avons extraits. Un visage 3D a trois degrés de liberté : le roulis, le lacet et le tangage. Le roulis est estimé grâce à la maximisation d’une fonction d’énergie horizontale globale au visage. Il correspond à la rotation qui s’effectue parallèlement au plan de l’image. Il est donc possible de le corriger pour qu’il soit nul, contrairement aux autres rotations. Enfin, nous proposons un algorithme de suivi de visage basé sur le suivi des yeux dans une séquence vidéo. Ce suivi repose sur la maximisation de la corrélation des cartes d’énergie binarisées ainsi que sur le suivi des éléments connexes de cette carte binaire. L’ensemble de ces trois méthodes permet alors tout d’abord d’évaluer la pose d’un visage qui se trouve dans une trame donnée puis de lier tous les visages d’une même personne dans une séquence vidéo, pour finalement extraire plusieurs exemplaires de ce visage afin de les soumettre à un algorithme de reconnaissance du visage. / The aim of this thesis is to create a methodology in order to extract one or a few representative face images of a video sequence with a view to apply a face recognition algorithm. A video is a media particularly rich. Among all the objects present in the video, human faces are, for sure, the most salient objects. Let us consider a video sequence where each frame contains a face of the same person. The primary assumption of this thesis is that some samples of this face are better than the others in terms of face recognition. A face is a non-rigid 3D object that is projected on a plan to form an image. Hence, the face appearance changes according to the relative positions of the camera and the face. Many works in the field of face recognition require faces as frontal as possible. To extract the most frontal face samples, on the one hand, we have to estimate the head pose. On the other hand, tracking the face is also essential. Otherwise, extraction representative face samples are senseless. This thesis contains three main parts. First, once a face has been detected in a sequence, we try to extract the positions and sizes of the eyes, the nose and the mouth. Our approach is based on local energy maps mainly with a horizontal direction. In the second part, we estimate the head pose using the relative positions and sizes of the salient elements detected in the first part. A 3D face has 3 degrees of freedom: the roll, the yaw and the pitch. The roll is estimated by the maximization of a global energy function computed on the whole face. Since this roll corresponds to the rotation which is parallel to the image plan, it is possible to correct it to have a null roll value face, contrary to other rotations. In the last part, we propose a face tracking algorithm based on the tracking of the region containing both eyes. This tracking is based on the maximization of a similarity measure between two consecutive frames. Therefore, we are able to estimate the pose of the face present in a video frame, then we are also able to link all the faces of the same person in a video sequence. Finally, we can extract several samples of this face in order to apply a face recognition algorithm on them.