Thesis--University of Helsinki, 1988. / Also published in : Proceedings of the Finnish Dental Society, 1988, vol. 84, Suppl. VI. Includes bibliographical references.

Thesis (B.A.)--Haverford College, Dept. of Psychology, 2006. / Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 2006. / Title from first page of PDF file (viewed February 27, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 143-148).

Thesis (Ph. D.)--University of California, San Diego, 2006. / Title from first page of PDF file (viewed July 24, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 87-91).

The face inversion effect (FIE; Yin, 1969) raises the question of whether upright face identification is mediated by a special mechanism that is disrupted by inversion. The present study investigates the effect of face inversion on the perceptual encoding of spatial frequency (SF) information using a novel variant of the Bubbles technique (Gosselin & Schyns, 2001). In Experiment 1, the SF Bubbles technique was validated using a simple plaid detection task. In Experiment 2, SF tuning of upright and inverted face identification was measured. While the data showed a clear FIE (28% higher accuracy and 455 ms shorter reaction times for upright faces), SF tunings were remarkably similar in both conditions (r = .96; a single SF band of ~2 octaves peaking at ~9 cycles per face width). Experiments 3 and 4 demonstrated that SF Bubbles is sensitive to bottom-up and top-down induced changes in SF tuning, respectively. Overall, the results show that the same SFs are utilized in upright and inverted face identification, albeit not with equal efficiency.

Thesis (MScEng) -- Stellenbosch University, 2003. / ENGLISH ABSTRACT: The video-based computer vision patient positioning system that is being developed at iThemba Laboratories, relies on the accurate, robust location, identification and tracking of a number of markers on the patient's mask. The precision requirements are demanding - a small error in the location of the markers leads to an inaccurate positioning of the patient, which could have fatal consequences. In this thesis we discuss the contsruction of suitable markers, their identification with subpixel accuracy, as well as a robust tracking algorithm. The algorithms were implemented and tested on real data. We also note and give examples of other applications, most notably 2D human face tracking and the 3D tracking of a moving person. / AFRIKAANSE OPSOMMING: Die video-gebaseerde rekenaarvisie pasiënt posisionerings stelsel wat by iThemba Laboratoriums ontwikkel word, maak staat op die akkurate opsporing, identifikasie en volging van 'n stel merkers op die pasiënt se masker. Die akkuraatheids voorwaardes is besonders streng - selfs 'n klein fout in die lokasie vandie merkers sal lei tot die onakkurate posisionering van die pasiënt, wat dodelike gevolge kan hê. In hierdie tesis bespreek ons die konstruksie van geskikte merkers, die identifikasie van die merkers tot op subbeeldingselement vlak en ook die akkurate volging van die merkers. Die algoritmes is op regte data getoets. Ander toepassings soos 2D en 3D menlike gesigs-volging word ook kortliks bespreek.

Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2007. / The face is one of the most characteristic parts of the human body and has been used by people for personal identification for centuries. In this thesis an automatic process for frontal face recognition from 2–dimensional images is presented based on principal component analysis. The goal is to use these concepts in eventual face–recognizing login software. The first step is detecting faces in images that are allowed a certain degree of clutter. This is achieved by skin colour detection in the HSV colourspace. This process indicates the area of the image most likely corresponding to the face. Extracting the face is achieved by morphological processing of this area of the image. The face is then normalized by a transformation that uses the eye coordinates as input. Automatic eye detection is implemented based on colour analysis of the facial images and a 91.1% success rate is achieved. Recognition of the normalized faces is achieved using eigenfaces. To calculate these, a large enough database of facial images is needed. The xm2vts database is used in this thesis as the images have very constant lighting conditions throughout – an important factor affecting the accuracy of the recognition stage. Distinction is also made between identification and verification of faces. For identification, up to 80.1% accuracy is achieved, while for verification, the equal error rate is approximately 3.5%.

This thesis discusses a novel method for modelling facial action units. It presents facial action units model based on boundary value problems for accurate representation of human facial expression in three-dimensions. In particular, a solution to a fourth order elliptic Partial Differential Equation (PDE) subject to suitable boundary conditions is utilized, where the chosen boundary curves are based on muscles movement defined by Facial Action Coding System (FACS). This study involved three stages: modelling faces, manipulating faces and application to simple facial animation. In the first stage, PDE method is used in modelling and generating a smooth 3D face. The PDE formulation using small sets of parameters contributes to the efficiency of human face representation. In the manipulation stage, a generic PDE face of neutral expression is manipulated to a face with expression using PDE descriptors that uniquely represents an action unit. A combination of the PDE descriptor results in a generic PDE face having an expression, which successfully modelled four basic expressions: happy, sad, fear and disgust. An example of application is given using simple animation technique called blendshapes. This technique uses generic PDE face in animating basic expressions.

In this thesis, I use a Darwinian approach to explore the role of sexual-dimorphism in human behaviour across development and into early adulthood, and its impact on adult mating-strategies. In Studies 1-2, I explore the importance of heredity and developmental stability of facial characteristics in light of theories of sexual selection. Using family photos, judgements of faces of parents and offspring revealed that facial sexual-dimorphism is passed on from father to son and mother to daughter, while inheritance of attractiveness is apparent only in daughters. I also examine the stability of facial appearance across development (infancy, childhood, young-adult), and find that sexual-dimorphism remains stable for both sexes, while attractiveness is stable only in females. Feminine characteristics are perceived as attractive in infant faces, and feminine characteristics have been perceived as looking more neotenous. In light of these data, I develop a theory for increased preferences for neoteny in human infants (Chapter 4). In studies 3-4, I examine the developmental milestones, puberty and first sexual intercourse in relationship to adult mate-choice strategies. Women who experience first coitus early prefer increased facial-masculinity in potential mates. In men, both early coitus and early puberty relate to increased preferences for facial-femininity in potential mates. In study 5, I look at attributions of peri-pubertal children (aged 11-12 years) to peer faces manipulated on sexually-dimorphic features. Children's attractiveness judgements show evidence of adult-like preferences, with boys preferring feminine girls' faces and girls preferring feminised boys' faces. Both girls and boys attribute the negative stereotype of bullying to more masculinised boys' faces. Study 6 concerns the relationship between two modalities of sexual-dimorphism, pheromones and facial characteristics. Adults indicate preferences from a continuum of sexually-dimorphic face-shapes, in addition to rating sex-specific pheromones on pleasantness. Individuals who prefer sex-typical facial characteristics in opposite-sex faces also judged the sex-typical pheromone as more pleasant.

Atualmente, o reconhecimento facial por técnicas automáticas é ainda uma tarefa desafiadora uma vez que as imagens faciais podem ser afetadas por mudanças na cena, tais como na iluminação, na pose da cabeça, ou na expressão facial. Além disso, a representação de faces por feições faciais geralmente requer diversas dimensões, o que impõe desafios adicionais ao reconhecimento facial. Nessa tese, é proposto um novo método de reconhecimento facial com o objetivo de ser robusto a muitos dos fatores que podem afetar as feições faciais na prática e se baseia em determinar transformações do espaço original de feições faciais de alta dimensionalidade para um espaço de baixa dimensionalidade que apresenta maior discriminação das classes de dados faciais (indivíduos). Isso é realizado através da aplicação de um método Projeções Ortogonais Preservadoras de Localidade (Orthogonal Locality Preserving Projections - OLPP) modificado, que usa esquemas de definição de localidade supervisionados que têm o objetivo de preservar a estrutura das classes de dados faciais no espaço resultante de baixa dimensionalidade, diferentemente do método OLPP típico que preserva a estrutura dos dados faciais. Dessa forma, as classes se tornam mais compactas, preservando a métrica de classificação. O método proposto pode trabalhar tanto com representações densas como esparsas de imagens faciais (ou seja, ele pode usar subconjuntos ou todos os pixels das imagens faciais), sendo proposto nessa tese um método de extração de feições faciais esparsas e um método de extração de feições faciais densas que preservam a informação de cor das imagens faciais apresentando melhora em relação ao método OLPP típico que usa imagens em escalas de cinza em baixa resolução. Novas imagens faciais de teste são classificadas no espaço de baixa dimensionalidade obtido usando Máquinas de Vetores de Suporte (Support Vector Machines - SVM) treinadas com margens suaves, apresentando maior eficiência do que a regra do vizinho mais próximo usada no método OLPP típico. Um conjunto de experimentos foi projetado para avaliar o método proposto sob várias condições encontradas na prática (como mudanças na pose, expressão facial, iluminação e a presença de artefatos que causam oclusão facial). Os resultados experimentais foram obtidos usando cinco bases de imagens faciais públicas (a PUT, a FEI, a FERET, a Yale e a ORL). Esses experimentos confirmam que os esquemas propostos de extração de feições faciais integrados à transformação proposta para um espaço discriminativo de baixa dimensionalidade empregando o esquema alternativo de classificação usando SVM com margens suaves obtêm maiores taxas de reconhecimento do que o próprio método OLPP e métodos representativos do estado da arte mesmo quando são usadas imagens coloridas em alta resolução (das bases de imagens faciais PUT, FEI e FERET) como imagens faciais em escalas de cinza em baixa resolução (das bases Yale e ORL). / Nowadays, face recognition by automatic techniques still is a challenging task since face images may be affected by changes in the scene, such as in the illumination, head pose or face expression. Also, face feature representation often requires several dimensions, which poses additional challenges for face recognition. In this thesis is proposed a novel face recognition method with the objective of to be robust to many issues which can affect the face features in practice and it is based on projections of high dimensional face image representations into lower dimensionality and highly discriminative spaces. This is achieved by a modified Orthogonal Locality Preserving Projections (OLPP) method that uses a supervised alternative locality definition scheme designed to preserve the face class (individuals) structure in the obtained lower dimensionality face feature space unlike the typical OLPP method which preserves the face data structure. Besides, a new kernel equation is proposed to calculate affinities among face samples, presenting better class structure preservation when compared to the heat kernel used by the typical OLPP method. The proposed method can work with sparse and dense face image representations (i.e. it can use sub-sets or all face image pixels), and a sparse and a dense feature extraction methods are proposed, which preserve the color information during the feature extraction process from the facial images improving on the typical OLPP method which uses grayscale low-resolution face images. New test face images are classified in the obtained lower dimensionality feature space using a trained soft margins Support Vector Machine (SVM), so it performs better than the nearest neighbor rule used in the typical OLPP method. A set of experiments was designed to evaluate the proposed method under various conditions found in practice (such as changes in head pose, face expression, illumination, and in the presence of occlusion artifacts). The experimental results were obtained using five challenging public face databases (namely, PUT, FEI, FERET, Yale and ORL). These experiments confirm that the proposed feature extraction method integrated to the proposed transformation to a discriminative lower dimensionality space using the alternative classification scheme with SVM and soft margins obtains higher recognition rates than the OLPP method itself and methods representative of the state-ofthe- art even when are used color (RGB) face images in high resolution (PUT, FEI and FERET face databases) as well as grayscale face images in low resolution (Yale and ORL face databases).