The development of norm-based coding and category-specific face prototypes : an examination of 5- and 8-year-olds' face space

The present set of experiments was designed to investigate the organization and refmement of young children's face space. Past research has demonstrated that adults encode individual faces in reference to a distinct face prototype that represents the average of all faces ever encountered. The prototype is not a static abstracted norm but rather a malleable face average that is continuously updated by experience (Valentine, 1991); for example, following prolonged viewing of faces with compressed features (a technique referred to as adaptation), adults rate similarly distorted faces as more normal and more attractive (simple attractiveness aftereffects). Recent studies have shown that adults possess category-specific face prototypes (e.g., based on race, sex). After viewing faces from two categories (e.g., Caucasian/Chinese) that are distorted in opposite directions, adults' attractiveness ratings simultaneously shift in opposite directions (opposing aftereffects). The current series of studies used a child-friendly method to examine whether, like adults, 5- and 8-year-old children show evidence for category-contingent opposing aftereffects. Participants were shown a computerized storybook in which Caucasian and Chinese children's faces were distorted in opposite directions (expanded and compressed). Both before and after adaptation (i.e., reading the storybook), participants judged the normality/attractiveness of a small number of expanded, compressed, and undistorted Caucasian and Chinese faces. The method was first validated by testing adults (Experiment I ) and was then refined in order to test 8- (Experiment 2) and 5-yearold (Experiment 4a) children. Five-year-olds (our youngest age group) were also tested in a simple aftereffects paradigm (Experiment 3) and with male and female faces distorted in opposite directions (Experiment 4b). The current research is the first to demonstrate evidence for simple attractiveness aftereffects in children as young as 5, thereby indicating that similar to adults, 5-year-olds utilize norm-based coding. Furthermore, this research provides evidence for racecontingent opposing aftereffects in both 5- and 8-year-olds; however, the opposing aftereffects demonstrated by 5-year-olds were driven largely by simple aftereffects for Caucasian faces. The lack of simple aftereffects for Chinese faces in 5-year-olds may be reflective of young children's limited experience with other-race faces and suggests that children's face space undergoes a period of increasing differentiation over time with respect to race. Lastly, we found no evidence for sex -contingent opposing aftereffects in 5-year-olds, which suggests that young children do not rely on a fully adult-like face space even for highly salient face categories (i.e., male/female) with which they have comparable levels of experience.

The Role of Experience in the Organization and Refinement of Face Space

Adults code faces in reference to category-specific norms that represent the different face categories encountered in the environment (e.g., race, age). Reliance on such norm-based coding appears to aid recognition, but few studies have examined the development of separable prototypes and the way in which experience influences the refinement of the coding dimensions associated with different face categories. The present dissertation was thus designed to investigate the organization and refinement of face space and the role of experience in shaping sensitivity to its underlying dimensions. In Study 1, I demonstrated that face space is organized with regard to norms that reflect face categories that are both visually and socially distinct. These results provide an indication of the types of category-specific prototypes that can conceivably exist in face space. Study 2 was designed to investigate whether children rely on category-specific prototypes and the extent to which experience facilitates the development of separable norms. I demonstrated that unlike adults and older children, 5-year-olds rely on a relatively undifferentiated face space, even for categories with which they receive ample experience. These results suggest that the dimensions of face space undergo significant refinement throughout childhood; 5 years of experience with a face category is not sufficient to facilitate the development of separable norms. In Studies 3 through 5, I examined how early and continuous exposure to young adult faces may optimize the face processing system for the dimensions of young relative to older adult faces. In Study 3, I found evidence for a young adult bias in attentional allocation among young and older adults. However, whereas young adults showed an own-age recognition advantage, older adults exhibited comparable recognition for young and older faces. These results suggest that despite the significant experience that older adults have with older faces, the early and continuous exposure they received with young faces continues to influence their recognition, perhaps because face space is optimized for young faces. In Studies 4 and 5, I examined whether sensitivity to deviations from the norm is superior for young relative to older adult faces. I used normality/attractiveness judgments as a measure of this sensitivity; to examine whether biases were specific to norm-based coding, I asked participants to discriminate between the same faces. Both young and older adults were more accurate when tested with young relative to older faces—but only when judging normality. Like adults, 3- and 7-year-olds were more accurate in judging the attractiveness of young faces; however, unlike adults, this bias extended to the discrimination task. Thus by 3 years of age children are more sensitive to differences among young relative to older faces, suggesting that young children's perceptual system is more finely tuned for young than older adult faces. Collectively, the results of this dissertation help elucidate the development of category-specific norms and clarify the role of experience in shaping sensitivity to the dimensions of face space.

Learning Boltzmann distance metric for face recognition

We introduce a new method for face recognition using a versatile probabilistic model known as Restricted Boltzmann Machine (RBM). In particular, we propose to regularise the standard data likelihood learning with an information-theoretic distance metric defined on intra-personal images. This results in an effective face representation which captures the regularities in the face space and minimises the intra-personal variations. In addition, our method allows easy incorporation of multiple feature sets with controllable level of sparsity. Our experiments on a high variation dataset show that the proposed method is competitive against other metric learning rivals. We also investigated the RBM method under a variety of settings, including fusing facial parts and utilising localised feature detectors under varying resolutions. In particular, the accuracy is boosted from 71.8% with the standard whole-face pixels to 99.2% with combination of facial parts, localised feature extractors and appropriate resolutions.

Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views.

Cartografiar el acontecimiento. Paisajes eventuales

En este trabajo se pretende hacer un acercamiento al estudio del paisaje desde su perspectiva dual, espacial y temporal. Se propone un retorno a la geografía y la defensa del espacio como objeto de estudio al tiempo que se mantiene una lectura temporal, eventual, del paisaje. Se trata de investigar las formas en que al enfrentarse al espacio no dominado se trazan estrategias de dominación y medida. Para ello se han estudiado dos casos de estudio: la Batalla de Berlín durante la Segunda Guerra Mundial y el lago Salton en California. This paper aims to make an approach to the study of landscape from its dual condition in space and time. It proposes a return to the geography and defense as a study space while maintaining a temporal reading, eventually, the landscape. The research focuses on the strategies of domination and plotted as the face space. This has been studied for two case studies: the Battle of Berlin during the Second World War and the Salton lake in California

A face detection algorithm based on high dimensional space geometry

In this paper, a face detection algorithm which is based on high dimensional space geometry has been proposed. Then after the simulation experiment of Euclidean Distance and the introduced algorithm, it was theoretically analyzed and discussed that the proposed algorithm has apparently advantage over the Euclidean Distance. Furthermore, in our experiments in color images, the proposed algorithm even gives more surprises.

Individual stable space : an approach to face recognition under uncontrolled conditions

There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a ldquodivide and conquerrdquo strategy, this paper attempts to directly address face recognition under uncontrolled conditions. The key is the individual stable space (ISS), which only expresses personal characteristics. A neural network named ISNN is proposed to map a raw face image into the ISS. After that, three ISS-based algorithms are designed for FR under uncontrolled conditions. There are no restrictions for the images fed into these algorithms. Moreover, unlike many other FR techniques, they do not require any extra training information, such as the view angle. These advantages make them practical to implement under uncontrolled conditions. The proposed algorithms are tested on three large face databases with vast variations and achieve superior performance compared with other 12 existing FR techniques.

Writing the self : the emergence of a dialogic space

While much narrative inquiry is concerned with issues of self and identity, doing study on the processes (the how) of self-making offers ongoing challenges to methodology. This article explores the creation of a dialogic space that assisted young adolescents to write about themselves and their daily lives using email journals as an alternative to face-to-face interviews. With the researcher acting as a listener-responder, and in the absence of researcher-designed questions, a dynamic field was opened up for participant-led self-making to emerge over a six month period of self-reflective written expression. The article describes a shared email relationship based on a dialogic pattern of thinking, writing, listening and response intended to foster participants’ voices as ontological narratives of self. Findings show the use of email journals created a synergy for self-disclosure and a safe space for self-expression where the willingness of participants to be themselves was encouraged. The self-representations of a specific group of gifted young adolescents thus emerged as written versions of “who” they are —offering data that differs from interview approaches and contributing to discussion of the value of ontology narratives.

Young people, public space and new urbanism

The first of three articles in this issue addressing the public space topic considers public space and young people in the light of a range of papers delivered at the 27th International Conference on 'Making Cities Livable', held in Vienna, Austria, in July 2000. Under the overarching concept of the "liveable city" the conference themes of 'Rediscovery of public space' and 'Cities for the wellbeing of children' attracted a broad mix of those interested in the planning, design and management of urban space. A number of themes percolated through the conference which stimulated the writers to examine the nexus between urban development, young people and public space. There is an ongoing need to examine the meaning of public space in the face of powerful urban development trends. A model of public space practice is required which incorporates a vision of inclusive public spaces, fosters the interactivity of design, planning, social policy and management, and resources for greater communication and strategic action between stake holders from the most local of levels to those at state and international levels. The speed and magnitude of contemporary urban development makes community input and influence difficult, particularly for those impacted on by the exclusionary tendencies of much urban development. It is critical that a range of meaningful and sustainable mechanisms are developed which allow young people’s conceptions of what constitutes youth-friendly space to be directly made and taken account of.

Time, place and space – student group collaboration using Google Drive

Virtual working environments are intrinsic to the contemporary workplace and collaborative skills are a vital graduate capability. To develop students’ collaborative skills, first year medical laboratory science students undertake a group poster project, based on a blended learning model. Learning is scaffolded in lectures, workshops in collaborative learning spaces, practitioner mentoring sessions, and online resources. Google Drive provides an online collaborative space for students to realise tangible outcomes from this learning. A Google Drive document is created for each group and shared with members. In this space, students assign tasks and plan workflow, share research, progressively develop poster content, reflect and comment on peer contributions and use the messaging functions to ‘talk’ to group members. This provides a readily accessible, transparent record of group work, crucial in peer assessment, and a communication channel for group members and the lecturer, who can support groups if required. This knowledge creation space also augments productivity and effectiveness of face-to-face collaboration. As members are randomly allocated to groups and are often of diverse backgrounds and unknown to each other, resilience is built as students navigate the uncertainties and complexities of group dynamics, learning to focus on the goal of the team task as they constructively and professionally engage in team dialogue. Students are responsible and accountable for individual and group work. The use of Google Drive was evaluated in a survey including Likert scale and open ended qualitative questions. Statistical analysis was carried out. Results show students (79%) valued the inclusion of online space in collaborative work and highly appreciated (78%) the flexibility provided by Google Drive, while recognising the need for improved notification functionality. Teaching staff recognised the advantages in monitoring and moderating collaborative group work, and the transformational progression in student collaborative as well as technological skill acquisition, including professional dialogue.

3D shape extraction of human face in presence of facial hair: A profilometric approach

The increasing use of 3D modeling of Human Face in Face Recognition systems, User Interfaces, Graphics, Gaming and the like has made it an area of active study. Majority of the 3D sensors rely on color coded light projection for 3D estimation. Such systems fail to generate any response in regions covered by Facial Hair (like beard, mustache), and hence generate holes in the model which have to be filled manually later on. We propose the use of wavelet transform based analysis to extract the 3D model of Human Faces from a sinusoidal white light fringe projected image. Our method requires only a single image as input. The method is robust to texture variations on the face due to space-frequency localization property of the wavelet transform. It can generate models to pixel level refinement as the phase is estimated for each pixel by a continuous wavelet transform. In cases of sparse Facial Hair, the shape distortions due to hairs can be filtered out, yielding an estimate for the underlying face. We use a low-pass filtering approach to estimate the face texture from the same image. We demonstrate the method on several Human Faces both with and without Facial Hairs. Unseen views of the face are generated by texture mapping on different rotations of the obtained 3D structure. To the best of our knowledge, this is the first attempt to estimate 3D for Human Faces in presence of Facial hair structures like beard and mustache without generating holes in those areas.

Bayesian parameter identification in dynamic state space models using modified measurement equations

When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. The present study proposes strategies based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples cover laboratory studies on a time variant dynamical system and a bending-torsion coupled, geometrically non-linear building frame under earthquake support motions. (C) 2015 Elsevier Ltd. All rights reserved.

Low Resolution Face Recognition Across Variations in Pose and Illumination

We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.