<p>Gaze tracking means to detect and follow the direction in which a person looks. This can be used in for instance human-computer interaction. Most existing systems illuminate the eye with IR-light, possibly damaging the eye. The motivation of this thesis is to develop a truly non-intrusive gaze tracking system, using only a digital camera, e.g. a web camera.</p><p>The approach is to detect and track different facial features, using varying image analysis techniques. These features will serve as inputs to a neural net, which will be trained with a set of predetermined gaze tracking series. The output is coordinates on the screen.</p><p>The evaluation is done with a measure of accuracy and the result is an average angular deviation of two to four degrees, depending on the quality of the image sequence. To get better and more robust results, a higher image quality from the digital camera is needed.</p>

<p>Traditional models of usability are not sufficient for software in the home, since they are built with office software in mind. Previous research suggest that social issues among other things, separate software in homes from software in offices. In order to explore that further, the use qualities to design for, in software for use in face-to-face meetings at home were contrasted to such systems at offices. They were studied using a pluralistic model of <em>use quality</em> with roots in socio-cultural theory, cognitive systems engineering, and architecture. The research approach was interpretative design cases. Observations, situated interviews, and workshops were conducted at a Swedish bank, and three interactive television appliances were designed and studied in simulated home environments. It is concluded that the use qualities to design for in infotainment services on interactive television are <em>laidback interaction, togetherness</em> among users, and <em>entertainment</em>. This is quite different from bank office software that usually is characterised by not only traditional usability criteria such as learnability, flexibility, effectiveness, efficiency, and satisfaction, but also professional <em>face management</em> and <em>ante-use</em>. Ante-use is the events and activities that precedes the actual use that will set the ground for whether the software will have quality in use or not. Furthermore, practices for how to work with <em>use quality values, use quality objectives</em>, and <em>use quality criteria</em> in the interaction design process are suggested. Finally, future research in design of software for several co-present users is proposed.</p> / Report code: LiU-Tek-Lic-2002:61.

<p>This thesis aims at building a system capable of automatically extracting and parameterizing the position of a face and its features in images acquired from a low-end monocular camera. Such a challenging task is justified by the importance and variety of its possible applications, ranging from face and expression recognition to animation of virtual characters using video depicting real actors. The implementation includes the construction of Active Appearance Models of the human face from training images. The existing face model Candide-3 is used as a starting point, making the translation of the tracking parameters to standard MPEG-4 Facial Animation Parameters easy.</p><p>The Inverse Compositional Algorithm is employed to adapt the models to new images, working on a subspace where the appearance is "projected out" and thus focusing only on shape.</p><p>The algorithm is tested on a generic model, aiming at tracking different people’s faces, and on a specific model, considering one person only. In the former case, the need for improvements in the robustness of the system is highlighted. By contrast, the latter case gives good results regarding both quality and speed, with real time performance being a feasible goal for future developments.</p>

No description available.

<p>Syftet med uppsatsen var att undersöka om ansiktsattraktivitet hänger samman med kvalitet i vänskapsrelationer. Hypotesen utgick ifrån att likhet i ansiktsattraktivitet påverkar valet av viktigaste vän samt att en låg skillnad i ansiktsattraktivitet mellan personerna gynnar relationskvaliteten. Tidigare forskning har antingen undersökt attraktivitet eller vänskapsrelationer. Vår uppsats knyter samman dessa två faktorer. 150 tonårspojkars ansiktsattraktivitet bedömdes med hjälp av ett eget utformat instrument. Data på pojkarnas vänskapsrelationer från en longitudinell studie användes för jämförelser. Högt skattad ansiktsattraktivitet visade i vår studie ha ett samband med konflikter i vänskapsrelationen. Attraktiviteten visade sig inte spela någon roll i valet av viktigaste vän. Däremot har attraktiviteten en betydelse för vänskapskvaliteten, då hög ansiktsattraktivitet påverkar vänskapskvaliteten negativt.</p> / <p>The purpose of this thesis was to investigate if facial attractiveness and quality in relationships are connected. The hypothesis was that equality in facial attractiveness matters in people’s choices of important friends and that low difference in facial attractiveness between the people profits quality in their friendship. Earlier research has either been focusing on attractiveness or quality in friendships. This thesis links these two factors together. The facial attractiveness of 150 teenage boys was judged with an instrument made by us. Data, with information about the boys’ relationships, from a longitudinal study were used for comparisons. We found that high facial attractiveness is related to conflicts in friendships, but that attractiveness does not matter in the choice of important friends. However, attractiveness does matter in friendship quality, since high attractiveness affects the quality in a negative way.</p>

This thesis presents a general, trainable system for object detection in static images and video sequences. The core system finds a certain class of objects in static images of completely unconstrained, cluttered scenes without using motion, tracking, or handcrafted models and without making any assumptions on the scene structure or the number of objects in the scene. The system uses a set of training data of positive and negative example images as input, transforms the pixel images to a Haar wavelet representation, and uses a support vector machine classifier to learn the difference between in-class and out-of-class patterns. To detect objects in out-of-sample images, we do a brute force search over all the subwindows in the image. This system is applied to face, people, and car detection with excellent results. For our extensions to video sequences, we augment the core static detection system in several ways -- 1) extending the representation to five frames, 2) implementing an approximation to a Kalman filter, and 3) modeling detections in an image as a density and propagating this density through time according to measured features. In addition, we present a real-time version of the system that is currently running in a DaimlerChrysler experimental vehicle. As part of this thesis, we also present a system that, instead of detecting full patterns, uses a component-based approach. We find it to be more robust to occlusions, rotations in depth, and severe lighting conditions for people detection than the full body version. We also experiment with various other representations including pixels and principal components and show results that quantify how the number of features, color, and gray-level affect performance.

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system.

abstract With many visual speech animation techniques now available, there is a clear need for systematic perceptual evaluation schemes. We describe here our scheme and its application to a new video-realistic (potentially indistinguishable from real recorded video) visual-speech animation system, called Mary 101. Two types of experiments were performed: a) distinguishing visually between real and synthetic image- sequences of the same utterances, ("Turing tests") and b) gauging visual speech recognition by comparing lip-reading performance of the real and synthetic image-sequences of the same utterances ("Intelligibility tests"). Subjects that were presented randomly with either real or synthetic image-sequences could not tell the synthetic from the real sequences above chance level. The same subjects when asked to lip-read the utterances from the same image-sequences recognized speech from real image-sequences significantly better than from synthetic ones. However, performance for both, real and synthetic, were at levels suggested in the literature on lip-reading. We conclude from the two experiments that the animation of Mary 101 is adequate for providing a percept of a talking head. However, additional effort is required to improve the animation for lip-reading purposes like rehabilitation and language learning. In addition, these two tasks could be considered as explicit and implicit perceptual discrimination tasks. In the explicit task (a), each stimulus is classified directly as a synthetic or real image-sequence by detecting a possible difference between the synthetic and the real image-sequences. The implicit perceptual discrimination task (b) consists of a comparison between visual recognition of speech of real and synthetic image-sequences. Our results suggest that implicit perceptual discrimination is a more sensitive method for discrimination between synthetic and real image-sequences than explicit perceptual discrimination.

Etude de la dynamique temporelle de la catégorisation faciale depuis la détection d'un visage dans le champ visuel jusqu'a l'encodage d'une représentation individuelle du visage. Investigation de cette dynamique temporelle via l'électrophysiologie chez le sujet humain (potentiels évoqués). / Temporal dynamics of face visual categorization : an electrophysiological (event-related potentials) approach