It is our hypothesis that improvised musical interaction will be able to provide the extended engagement often failing others during long term Human Robot Interaction (HRI) trials. Our previous work found that simply framing sessions with their drumming robot Mortimer as social interactions increased both social presence and engagement, two factors we feel are crucial to developing and maintaining a positive and meaningful relationship between human and robot. For this study we investigate the inclusion of the additional social modalities, namely head pose and facial expression, as nonverbal behaviour has been shown to be an important conveyor of information in both social and musical contexts. Following a 6 week experimental study using automatic behavioural metrics, results demonstrate those subjected to nonverbal behaviours not only spent more time voluntarily with the robot, but actually increased the time they spent as the trial progressed. Further, that they interrupted the robot less during social interactions and played for longer uninterrupted. Conversely, they also looked at the robot less in both musical and social contexts. We take these results as support for open ended musical activity providing a solid grounding for human robot relationships and the improvement of this by the inclusion of appropriate nonverbal behaviours.

With a view to studying the development of social relationships between humans and robots, it is our contention that music can help provide extended engagement and open ended interaction. In this paper we explore the effectiveness of music as a mode of engagement using Mortimer, a robot able to play a drum kit and employing a composition algorithm to respond to a human pianist. We used this system to conduct a comparative study into the effects of presenting the robot as a social actor or as an instrument. Using automated behavioural metrics, including face tracking, we found that participants in the social actor condition played for longer uninterrupted and stopped the robot mid-performance less. They also spent more time looking at the robot when not playing and less time looking at the piano when playing. We suggest these results indicate greater fluency of playing and engagement and more feelings of social presence towards the robot when presented as a social actor.

Social perception abilities are among the most important skills necessary for robots to engage humans in natural forms of interaction. Affect-sensitive robots are more likely to be able to establish and maintain believable interactions over extended periods of time. Nevertheless, the integration of affect recognition frameworks in real-time human-robot interaction scenarios is still underexplored. In this article, we propose and evaluate a context-sensitive affect recognition framework for a robotic game companion for children. The robot can automatically detect affective states experienced by children in an interactive chess game scenario. The affect recognition framework is based on the automatic extraction of task features and social interaction-based features. Vision-based indicators of the children’s nonverbal behaviour are merged with contextual features related to the game and the interaction and given as input to support vector machines to create a context-sensitive multimodal system for affect recognition. The affect recognition framework is fully integrated in an architecture for adaptive human-robot interaction. Experimental evaluation showed that children’s affect can be successfully predicted using a combination of behavioural and contextual data related to the game and the interaction with the robot. It was found that contextual data alone can be used to successfully predict a subset of affective dimensions, such as interest toward the robot. Experiments also showed that engagement with the robot can be predicted using information about the user’s valence, interest and anticipatory behaviour. These results provide evidence that social engagement can be modelled as a state consisting of affect and attention components in the context of the interaction.

It is widely agreed that there is a need to excite more school students about computing. Considering teachers' views about student engagement is important to securing their support for any solution. We therefore present the results of a qualitative, questionnaire-based study on teachers' perceptions of the best ways to make the subject interesting. From 115 responses by UK computing teachers emerged a range of themes about the issues they felt were most important. We found that whilst their views reflected a range of approaches that are widely promoted in the literature and in national initiatives, there were also disconnects between teachers' views and wider discourses. Based on the results, we give specific recommendations for areas where more should be done to support teachers in making computing interesting to school students. Academics should do more to engage with teachers, especially if they wish to introduce deep computing principles in schools. Teachers expressed an interest in computing clubs in schools, but a strong support network for them is still needed. This may be an opportunity for businesses and universities to help support teachers.

There are a variety of initiatives to attract secondary school students to computer science. cs4fn is one such project. It combines a magazine, website and live shows, telling stories about computer science in spirited and creative ways. Here we focus on the use of the magazine and, using sociolinguistic discourse analysis, we analyze comments from students and teachers to understand why they have requested (free) subscriptions to the magazine and how they plan to use it. Our analysis shows that both students and teachers are attracted to the flexibility that cs4fn provides, and use it in a variety of learning contexts. We find that the flexibility of the magazine makes it a valuable tool to engage students and teachers and that they use it to further enthuse others (i.e., other students and teachers). We suggest that cs4fn magazine is a powerful form of outreach and that this approach can be widely disseminated within computer science and other academic disciplines, raising the profile of computing to both students and teachers, and spreading enthusiasm for computer science.

Successful human interaction commonly involves prototypical exchanges where interactors are engaged, synchronized, and harmonious in their behaviors. The copying of aspects of the other's behavior, at different levels, seems central to establishing and maintaining such empathic connections. Yet, many questions remain unanswered, particularly how it is possible to reflect the same affective content back to the other when the actual motion itself is not exactly the same as theirs. This paper presents a perceptual study in which emotional gestures conducted by an actor were mapped onto synthesized versions generated by an embodied virtual agent. Copying is at the expressive level, where qualities such as the fluidity or expansiveness of gestures are considered, rather than exact low-level motion matching. Participants were later asked to rate the emotional content of video recordings of both the original and the synthesized gestures. A statistical analysis shows that, in most cases, participants associated the emotional content of the agent's gestures with that intended to be expressed by the original actor. The results suggest that a combination of the type of movement performed and its quality is important for successfully communicating emotions.

The design of an affect recognition system for socially perceptive robots relies on representative data: human-robot interaction in naturalistic settings requires an affect recognition system to be trained and validated with contextualised affective expressions, that is, expressions that emerge in the same interaction scenario of the target application. In this paper we propose an initial computational model to automatically analyse human postures and body motion to detect engagement of children playing chess with an iCat robot that acts as a game companion. Our approach is based on vision-based automatic extraction of expressive postural features from videos capturing the behaviour of the children from a lateral view. An initial evaluation, conducted by training several recognition models with contextualised affective postural expressions, suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making our approach suitable for integration into an affect recognition system for a game companion in a real world scenario.

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.

The Inter-ACT (INTEracting with Robots - Affect Context Task) corpus is an affective and contextually rich multimodal video corpus containing affective expressions of children playing chess with an iCat robot. It contains videos that capture the interaction from different perspectives and includes synchronised contextual information about the game and the behaviour displayed by the robot. The Inter-ACT corpus is mainly intended to be a comprehensive repository of naturalistic and contextualised, task-dependent data for the training and evaluation of an affect recognition system in an educational game scenario. The richness of contextual data that captures the whole human-robot interaction cycle, together with the fact that the corpus was collected in the same interaction scenario of the target application, make the Inter-ACT corpus unique in its genre.

Affect sensitivity is of the utmost importance for a robot companion to be able to display socially intelligent behaviour, a key requirement for sustaining long-term interactions with humans. This paper explores a naturalistic scenario in which children play chess with the iCat, a robot companion. A person-independent, Bayesian approach to detect the user's engagement with the iCat robot is presented. Our framework models both causes and effects of engagement: features related to the user's non-verbal behaviour, the task and the companion's affective reactions are identified to predict the children's level of engagement. An experiment was carried out to train and validate our model. Results show that our approach based on multimodal integration of task and social interaction-based features outperforms those based solely on non-verbal behaviour or contextual information (94.79 % vs. 93.75 % and 78.13 %).

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.

We describe our experiences illustrating core concepts and enthusing children (age 11-17) about computer science through magic shows. We outline links between various tricks and computer science. The format of show we have trialed is to present real magic tricks with an underlying link to computer science. After each trick the audience is challenged to work out how it works. The mechanics are explained followed by the underlying computer science. Feedback with Talented and Gifted children has been exceptional. Informal feedback from younger children of varying ability has also been very positive.

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.

Vision-based human affect analysis is an interesting and challenging problem, impacting important applications in many areas. In this paper, beyond facial expressions, we investigate affective body gesture analysis in video sequences, a relatively understudied problem. Spatial-temporal features are exploited for modeling of body gestures. Moreover, we present to fuse facial expression and body gesture at the feature level using Canonical Correlation Analysis (CCA). By establishing the relationship between the two modalities, CCA derives a semantic “affect ” space. Experimental results demonstrate the effectiveness of our approaches. 1

Capturing and analyzing the correlations among facial parts are important for interpreting facial behaviors precisely. In this paper, we exploit Canonical Correlation Analysis (CCA) to model the correlations of facial parts for facial expression analysis. We propose a Matrix-based Canonical Correlation Analysis (MCCA) for better correlation analysis on 2D image or matrix data in general. Extensive experiments have shown that compared to the traditional CCA, MCCA models more accurately correlations among image data with more compact representation using much fewer canonical factors. 1

In this paper, we propose a novel Bayesian approach to modelling tem-poral transitions of facial expressions represented in a manifold, with the aim of dynamical facial expression recognition in image sequences. A gener-alised expression manifold is derived by embedding image data into a low dimensional subspace using Supervised Locality Preserving Projections. A Bayesian temporal model is formulated to capture the dynamic facial ex-pression transition in the manifold. Our experimental results demonstrate the advantages gained from exploiting explicitly temporal information in ex-pression image sequences resulting in both superior recognition rates and improved robustness against static frame-based recognition methods. 1

A fully automated, multistage system for real-time recognition of facial expression is presented. The system uses facial motion to characterize monochrome frontal views of facial expressions and is able to operate effectively in cluttered and dynamic scenes, recognizing the six emotions universally associated with unique facial expressions, namely happiness, sadness, disgust, surprise, fear, and anger. Faces are located using a spatial ratio template tracker algorithm. Optical flow of the face is subsequently determined using a real-time implementation of a robust gradient model. The expression recognition system then averages facial velocity information over identified regions of the face and cancels out rigid head motion by taking ratios of this averaged motion. The motion signatures produced are then classified using Support Vector Machines as either nonexpressive or as one of the six basic emotions. The completed system is demonstrated in two simple affective computing applications that respond in real-time to the facial expressions of the user, thereby providing the potential for improvements in the interaction between a computer user and technology.

This paper focuses on recognizing facial expressions at low resolution. We introduce local binary patterns (LBP) as novel low-computation discriminative features for low-resolution facial expression recognition. Compared to Gabor wavelets, LBP features can be derived rapidly in a single scan of raw images, whilst still retaining enough facial information in a compact representation. Support vector machine (SVM) is adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade database demonstrate that the LBP features are effective and efficient for facial expression recognition, and crucially perform robustly and stably over a useful range of low resolutions. Our method yields promising performance when processing compressed low-resolution video sequences from the PETS 2003 dataset.

This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. L ow-cost LBP features are introduced to effectively describle local fea tures of face images. A novel learning procedure, Conditional Mutual Infomation based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifie rs that maximize their mutual information about a candidate class, conditional to the response of any weak classifier already selected; a strong cl assifier is constructed by combining the learned weak classifiers using the Naive-Bayes. Extensive experiments on the Cohn-Kanade database illustrated that LBP features are effective for expression analysis, and CMIB enables much faster training than AdaBoost, and yields a classifier of improved c lassification performance.

This paper details an adaptive methodology that uses a genetic algorithm (GA) to deliver variations of a given mobile phone video game. The adaptive methodology uses the GA to generate an easier or harder game based on user-feedback of the desired level of difficulty. Game parameters are synthesised using empirically determined human user characteristics. The paper presents the results of a pilot study that confirm the success of the approach described herein.

A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, whilst still retaining facial information efficiently. Template matching with weighted Chi square statistic and support vector machine are adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade Database illustrate that the LBP features are effective and efficient for facial expression discrimination. Additionally, experiments on face images with different resolutions show that the LBP features are robust to low-resolution images, which is critical in real-world applications where only low-resolution video input is available.

An online biometric verification system for use over the Internet and requiring no specialist equipment is presented. Combining two distinct tests to ensure authenticity, a typing style test and a mouse-based signature test, achieves a fraudulent access rate of /spl ap/ 4.4 percent, while authentic users access with a rate of /spl ap/ 99 percent.

This paper presents a multi-differential neuromorphic approach to motion detection. The model is based evidence for a differential operators interpretation of the properties of the cortical motion pathway. We discuss how this strategy, which provides a robust measure of speed for a range of types of image motion using a single computational mechanism, forms a useful framework in which to develop future neuromorphic motion systems. We also discuss both our approaches to developing computational motion models, and constraints in the design strategy for transferring motion models to other domains of early visual processing.