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People play games now more than ever before. While the digital gaming industry dominates the market, boardgaming has been living in its shadow. Board games offer a physical tangibility and a social experience that can be found in few digital games.How do we create a tool that further builds upon those strengths?The purpose of this study is to examine the possibility of developing a service that consists of an mobile application and board game events to promote social face-to-face interaction. Focus has been on the social face-to-face interaction in the context of a board game session. We have through the use of service prototyping and low-fi prototypes created a small-scale context in which we have conducted our research in.This study has been conducted with the use of observations of board game events and with the use of low-fi prototypes.The thesis treats theories and methods that have been used throughout this project.We can also find the documentation of our research data, read about the following design process of creating the service and results of usertesting.Finally in the conclusion we present the results of our research, our final design and suggestions on further development.Keywords:

Yes / Can adults with autism spectrum disorders (ASD) infer what happened to someone from their emotional response? Millikan has argued that in everyday life, others' emotions are most commonly used to work out the antecedents of behavior, an ability termed retrodictive mindreading. As those with ASD show difficulties interpreting others' emotions, we predicted that these individuals would have difficulty with retrodictive mindreading. Sixteen adults with high-functioning autism or Asperger's syndrome and 19 typically developing adults viewed 21 video clips of people reacting to one of three gifts (chocolate, monopoly money, or a homemade novelty) and then inferred what gift the recipient received and the emotion expressed by that person. Participants' eye movements were recorded while they viewed the videos. Results showed that participants with ASD were only less accurate when inferring who received a chocolate or homemade gift. This difficulty was not due to lack of understanding what emotions were appropriate in response to each gift, as both groups gave consistent gift and emotion inferences significantly above chance (genuine positive for chocolate and feigned positive for homemade). Those with ASD did not look significantly less to the eyes of faces in the videos, and looking to the eyes did not correlate with accuracy on the task. These results suggest that those with ASD are less accurate when retrodicting events involving recognition of genuine and feigned positive emotions, and challenge claims that lack of attention to the eyes causes emotion recognition difficulties in ASD. / University of Nottingham, School of Psychology

Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioral traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. Our research considers the use of ears as a biometric for human recognition. Researchers have not considered this biometric as much as others, which include fingerprints, irises, and faces. This thesis presents a novel approach to recognize individuals based on their outer ear images through spatial segmentation. This approach to recognizing is also good for dealing with occlusions. The study will present several feature extraction techniques based on spatial segmentation of the ear image. The study will also present a method for classifier fusion. Principal components analysis (PCA) is used in this research for feature extraction and dimensionality reduction. For classification, nearest neighbor classifiers are used. The research also investigates the use of ear images as a supplement to face images in a multimodal biometric system. Our base eigen-ear experiment results in an 84% rank one recognition rate, and the segmentation method yielded improvements up to 94%. Face recognition by itself, using the same approach, gave a 63% rank one recognition rate, but when complimented with ear images in a multimodal system improved to 94% rank one recognition rate. / Master of Science

In dieser Arbeit steht die Frage im Vordergrund, inwieweit eine besondere Orientierung sozialen und pädagogischen Handelns, die Orientierung an Face, im hochschulischen Raum Spanien als handlungsleitendes Konstrukt aufgezeigt werden kann und inwieweit dies mit den in chinesischsprachigen Kulturräumen verorteten Konzepten von Face vergleichbar ist. Face wird als transkulturelles, individuell und kollektiv bedeutsames Orientierungsmuster verstanden, dessen Kenntnis und handlungswirksame Umsetzung die Grundlage pädagogisch bedeutsamer Internationalisierungsstrategien stellen kann. Das Grundanliegen dieser Arbeit ist der Versuch, auch für einen europäischen Sprach- und Kulturraum Face als soziale Orientierung, speziell im pädagogischen Handlungsfeld Hochschule, aufzuzeigen und daraus Handlungsräume für die Gestaltung studienfördernder Lern- und Lehrkonstellationen bei internationalen Studierenden zu entwerfen. Die vorliegende qualitativ-ethnomethodologische Datenerhebung besteht aus einer Kombination mehrerer Erhebungsinstrumente, die im Rahmen von zwei aufeinander aufbauenden Studien in Spanien und Deutschland verwendet wurden. Studie 1 ist eine Teilnehmende Beobachtung im Forschungsfeld in Kombination mit der Critical Incident Technique, Studie 2 besteht aus leitfadengestützten ethnographischen und problemzentrierten Interviews. Zentrale Ergebnisse der Datenauswertung sind, dass Face als relationales Konstrukt im Bereich der Lehr-/Lernstilforschung von hoher Relevanz ist. Der Ausdruck El qué dirán kann als spanische Entsprechung für Face angesehen werden. Die in diesem Zusammenhang entwickelten Modelle – das Vínculo-Modell und das Enchufe-Modell – bieten eine kultursensible Darstellung von Face. Von diesen Ergebnissen ausgehend werden Implikationen für eine Face-sensible Lehre aufgezeigt und Anregungen für die Gestaltung studienfördernder Lern- und Lehrkonstellationen bei internationalen Studierenden gegeben. / This paper focuses on the question to what extent a special orientation of social and pedagogic action, the orientation towards Face, can be shown to be a presumed construct in higher education in Spain and to what extent this can be connected to the concepts of Face observed in Chinese-speaking cultural areas. Face is understood as a transcultural, individually and collectively significant orientation pattern, the knowledge and effective implementation of which can form the basis of pedagogically significant internationalisation strategies. The basic aim of this paper is to attempt to identify Face as a social orientation for a European language and cultural area as well, especially in the field of higher education as an area of pedagogic action, and to use this as a basis for designing study-enhancing learning and teaching constellations among international students. The qualitative ethnomethodological data collection used consists of a combination of several survey instruments used in two consecutive studies in Spain and Germany. Study 1 is a participant observation in the research field in combination with the Critical Incident Technique; Study 2 consists of guideline-based ethnographic and problem-oriented interviews. Central results of the data evaluation are that Face is of high relevance as a relational construct in the field of teaching/learning style research. The expression El qué dirán can be regarded as the Spanish equivalent of Face. The models developed in this context – the Vínculo model and the Enchufe model - were taken from the data and offer a culturally sensitive representation of Face; they can be understood as a culture-specific supplement to the social orientation triangle (Henze 2011) for understanding social interaction. Based on these results, implications for face-sensitive teaching are identified and suggestions for the design of study-enhancing learning and teaching constellations for international students are made.

The focus of this thesis is on facial biometrics; specifically in the problems of face detection and face recognition. Despite intensive research over the last 20 years, the technology is not foolproof, which is why we do not see use of face recognition systems in critical sectors such as banking. In this thesis, we focus on three sub-problems in these two areas of research. Firstly, we propose methods to improve the speed-accuracy trade-off of the state-of-the-art face detector. Secondly, we consider a problem that is often ignored in the literature: to decrease the training time of the detectors. We propose two techniques to this end. Thirdly, we present a detailed large-scale study on self-updating face recognition systems in an attempt to answer if continuously changing facial appearance can be learnt automatically. / L'objectiu d'aquesta tesi és sobre biometria facial, específicament en els problemes de detecció de rostres i reconeixement facial. Malgrat la intensa recerca durant els últims 20 anys, la tecnologia no és infalible, de manera que no veiem l'ús dels sistemes de reconeixement de rostres en sectors crítics com la banca. En aquesta tesi, ens centrem en tres sub-problemes en aquestes dues àrees de recerca. En primer lloc, es proposa mètodes per millorar l'equilibri entre la precisió i la velocitat del detector de cares d'última generació. En segon lloc, considerem un problema que sovint s'ignora en la literatura: disminuir el temps de formació dels detectors. Es proposen dues tècniques per a aquest fi. En tercer lloc, es presenta un estudi detallat a gran escala sobre l'auto-actualització dels sistemes de reconeixement facial en un intent de respondre si el canvi constant de l'aparença facial es pot aprendre de forma automàtica.

In this thesis, the expression variation problem in two-dimensional (2D) and three-dimensional (3D) face recognition is tackled. While discriminant analysis (DA) methods are effective solutions for recognizing expression-variant 2D face images, they are not directly applicable when only a single sample image per subject is available. This problem is addressed in this thesis by introducing expression subspaces which can be used for synthesizing new expression images from subjects with only one sample image. It is proposed that by augmenting a generic training set with the gallery and their synthesized new expression images, and then training DA methods using this new set, the face recognition performance can be significantly improved. An important advantage of the proposed method is its simplicity; the expression of an image is transformed simply by projecting it into another subspace. The above proposed solution can also be used in general pattern recognition applications. The above method can also be used in 3D face recognition where expression variation is a more serious issue. However, DA methods cannot be readily applied to 3D faces because of the lack of a proper alignment method for 3D faces. To solve this issue, a method is proposed for sampling the points of the face that correspond to the same facial features across all faces, denoted as the closest-normal points (CNPs). It is shown that the performance of the linear discriminant analysis (LDA) method, applied to such an aligned representation of 3D faces, is significantly better than the performance of the state-of-the-art methods which, rely on one-by-one registration of the probe faces to every gallery face. Furthermore, as an important finding, it is shown that the surface normal vectors of the face provide a higher level of discriminatory information rather than the coordinates of the points. In addition, the expression subspace approach is used for the recognition of 3D faces from single sample. By constructing expression subspaces from the surface normal vectors at the CNPs, the surface normal vectors of a 3D face with single sample can be synthesized under other expressions. As a result, by improving the estimation of the within-class scatter matrix using the synthesized samples, a significant improvement in the recognition performance is achieved.

In this thesis, the expression variation problem in two-dimensional (2D) and three-dimensional (3D) face recognition is tackled. While discriminant analysis (DA) methods are effective solutions for recognizing expression-variant 2D face images, they are not directly applicable when only a single sample image per subject is available. This problem is addressed in this thesis by introducing expression subspaces which can be used for synthesizing new expression images from subjects with only one sample image. It is proposed that by augmenting a generic training set with the gallery and their synthesized new expression images, and then training DA methods using this new set, the face recognition performance can be significantly improved. An important advantage of the proposed method is its simplicity; the expression of an image is transformed simply by projecting it into another subspace. The above proposed solution can also be used in general pattern recognition applications. The above method can also be used in 3D face recognition where expression variation is a more serious issue. However, DA methods cannot be readily applied to 3D faces because of the lack of a proper alignment method for 3D faces. To solve this issue, a method is proposed for sampling the points of the face that correspond to the same facial features across all faces, denoted as the closest-normal points (CNPs). It is shown that the performance of the linear discriminant analysis (LDA) method, applied to such an aligned representation of 3D faces, is significantly better than the performance of the state-of-the-art methods which, rely on one-by-one registration of the probe faces to every gallery face. Furthermore, as an important finding, it is shown that the surface normal vectors of the face provide a higher level of discriminatory information rather than the coordinates of the points. In addition, the expression subspace approach is used for the recognition of 3D faces from single sample. By constructing expression subspaces from the surface normal vectors at the CNPs, the surface normal vectors of a 3D face with single sample can be synthesized under other expressions. As a result, by improving the estimation of the within-class scatter matrix using the synthesized samples, a significant improvement in the recognition performance is achieved.

Face recognition finds various applications in surveillance, Law enforcement etc. These applications require fast image processing in real time. Modern GPUs have evolved fully programmable parallel stream processors. The problem of face recognition in real time system is benefited by parallelism. With the aim of fulfilling both speed and accuracy criteria we present a GPU accelerated Face Recognition system. OpenCL is a heterogeneous computing language that allows extracting parallelism on different platforms like DSP processors, FPGAs, GPUs. The proposed kernel on GPU exploits coarse grain parallelism for Local Binary Pattern (LBP) histogram computation and ELTP (Enhanced Local Ternary Pattern) feature extraction. The proposed optimization techniques on local memory, work group size and work group dimension enhances the computation of face recognition on GPU further. As a result, we have achieved a speed up of 30 times to 300 times for 124*124 to 2048*2048 image sizes for LBP and ELTP feature extraction compared to CPU. We also present a robust real time face recognition and tracking on GPU using fusion of RGB and Depth images taken from Kinect sensor. The proposed segmentation after detection algorithm enhances the performances of recognition using LBP.