Learning gender from human gaits and faces

Computer vision based gender classification is an important component in visual surveillance systems. In this paper, we investigate gender classification from human gaits in image sequences, a relatively understudied problem. Moreover, we propose to fuse gait and face for improved gender discrimination. We exploit Canonical Correlation Analysis (CCA), a powerful tool that is well suited for relating two sets of measurements, to fuse the two modalities at the feature level. Experiments demonstrate that our multimodal gender recognition system achieves the superior recognition performance of 97.2 % in large datasets. In this paper, we investigate gender classification from human gaits in image sequences using machine learning methods. Considering each modality, face or gait, in isolation has its inherent weakness and limitations, we further propose to fuse gait and face for improved gender discrimination. We exploit Canonical Correlation Analysis (CCA), a powerful tool that is well suited for relating two sets of signals, to fuse the two modalities at the feature level. Experiments on large dataset demonstrate that our multimodal gender recognition system achieves the superior recognition performance of 97.2%. We plot in Figure 1 the flow chart of our multimodal gender recognition system. 1

Enthusing and inspiring with reusable kinaesthetic activities

We describe the experiences of three University projects that use a style of physical, non-computer based activity to enthuse and teach school students computer science concepts. We show that this kind of activity is effective as an outreach and teaching resource even when reused across different age/ability ranges, in lecture and workshop formats and for delivery by different people. We introduce the concept of a Reusable Outreach Object (ROO) that extends Reusable Learning Objects. and argue for a community effort in developing a repository of such objects

Eye Tracking as Interface for the Design of Generative Visual Forms and Patterns

When working with generative systems, designers enter into a loop of discrete steps; external evaluations of the output feedback into the system, and new outputs are subsequently reevaluated. In such systems, interacting low level elements can engender a difficult to predict emergence of macro-level characteristics. Furthermore, the state space of some systems can be vast. Consequently, designers generally rely on trial-and-error, experience or intuition in selecting parameter values to develop the aesthetic aspects of their de- signs. We investigate an alternative means of exploring the state spaces of generative visual systems by using a gaze- contingent display. A user's gaze continuously controls and directs an evolution of visual forms and patterns on screen. As time progresses and the viewer and system remain coupled in this evolution, a population of generative artefacts tends towards an area of their state space that is 'of interest', as defined by the eye tracking data. The evaluation-feedback loop is continuous and uninterrupted, gaze the guiding feedback mechanism in the exploration of state space. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down

Expressive Copying Behavior for Social Agents: A Perceptual Analysis

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Teaching HCI through magic

We discuss our use of magic to enthuse students about HCI and teach core concepts. We describe the format we trialed with student groups with a wide range of background from whole year groups to groups of gifted students. We conducted post-event surveys with students for two events and obtained teacher feedback from five others. We discuss the results of that data, showing that magic can be effective method for teaching HCI. The same tricks have since been used as part of an Interactive Systems Design MSc course

A study in engaging female students in computer science using role models

An effective approach to engaging young women to take computing in higher education is to provide examples of successful female computer scientists. Can a print publication that combines core computing concepts with inspiring stories of women in the field be effective? In this paper, we describe a campaign that distributed a 60 - page booklet on women in computing to UK secondary schools. We analyse the initial response from teachers, and draw some general conclusions from the project. Teachers expressed strong enthusiasm for the booklet, and also report the desire for recruitment and retention of girls in their computing programmes. They had confidence in the potential for this booklet to inspire young women to take computing

Teachers' perceptions of the value of research-based school lectures

A major challenge facing secondary schools is to encourage students to take computing courses. One approach is to invite external speakers from universities or industry to give lectures. The cs4fn project, a large UK-based initiative to enthuse students about computer science, includes this approach. Speakers from Queen Mary, University of London, visit schools to talk to students about computer science research. Our interactive talks tell engaging research-based stories on topics such as artificial intelligence and human-computer interaction as well as using magic tricks to illustrate computing principles. We asked teachers to complete post-talk surveys online; in particular we were interested in whether they believed students’ perceptions of the subject had changed. They reported that their students’ views of computer science were improved, and that they felt students were more likely to take classes in computing in the future as a result of the talk

FReD: the Floral Reflectance Database - a web portal for analyses of flower colour

Background: Flower colour is of great importance in various fields relating to floral biology and pollinator behaviour. However, subjective human judgements of flower colour may be inaccurate and are irrelevant to the ecology and vision of the flower's pollinators. For precise, detailed information about the colours of flowers, a full reflectance spectrum for the flower of interest should be used rather than relying on such human assessments. Methodology/Principal Findings: The Floral Reflectance Database (FReD) has been developed to make an extensive collection of such data available to researchers. It is freely available at http://www.reflectance.co.uk. The database allows users to download spectral reflectance data for flower species collected from all over the world. These could, for example, be used in modelling interactions between pollinator vision and plant signals, or analyses of flower colours in various habitats. The database contains functions for calculating flower colour loci according to widely-used models of bee colour space, reflectance graphs of the spectra and an option to search for flowers with similar colours in bee colour space. Conclusions/Significance: The Floral Reflectance Database is a valuable new tool for researchers interested in the colours of flowers and their association with pollinator colour vision, containing raw spectral reflectance data for a large number of flower species

Generating customised experimental stimuli for visual search using Genetic Algorithms shows evidence for a continuum of search efficiency

AbstractHere we present a new empirically validated computational paradigm that generates on demand stimuli with user-defined levels of salience for use in visual search tasks. Combining a Genetic Algorithm with a biologically motivated model for image saliency we are able to breed a range of customised texture elements for use in psychophysical experiments. We review these psychophysical studies, showing for the first time that an explicit and predictable continuum of search efficiency exists in human visual search