Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap)

Visualisation localisée en mouvement pour la natation

International audienceCompetitive sports coverage increasingly includes information on athlete or team statistics and records. Sports video coverage has traditionally embedded representations of this data in fixed locations on the screen, but more recently also attached representations to athletes or other targets in motion. These publicly used representations so far have been rather simple and systematic investigations of the research space of embedded visualizations in motion are still missing. Here we report on our preliminary research in the domain of professional and amateur swimming. We analyzed how visualizations are currently added to the coverage of Olympics swimming competitions and then plan to derive a design space for embedded data representations for swimming competitions. We are currently conducting a crowdsourced survey to explore which kind of swimming-related data general audiences are interested in, in order to identify opportunities for additional visualizations to be added to swimming competition coverage

Applications of Kort Spiral Learning Method on Learners Behaviour Based on Wavelet Transform Method(DWT) in E-Learning Environment

This paper is planning to address one of the important difficulties faced by the e-learning communities, that is, capturing of human emotion accurately both of a tutor and learner in e-learning sceanario. In this paper, an approach for human emotion recognition system based on Discrete Wavelet Transform (DWT) on korts spiral model of learning on learners and tutors is presented. The affective pedagogy is one of the important component in effective learning model. The Korts model helps us to understand the effectiveness of learners emotion in the learning environment. The Korts model can be better implemented by means of human emotion recognition system based on DWT method. The classification of human emotional state is achieved by extracting the energies from all sub-bands of DWT. The robust K-Nearest Neighbor (K-NN) is constructed for classification. The evaluation of the system is carried on using JApanese Female Facial Expression (JAFFE) database. Experimental results show that the proposed DWT based human emotion recognition system produces more accurate recognition rate which applied on Korts learning model we can able to produce the optimal e-learning environment(OELE)

The visual and beyond : characterizing experiences with auditory, haptic and visual data representations

Research in sonification and physicalization have expanded data representation techniques to include senses beyond the visual. Yet, little is known of how people interpret and make sense of haptic and sonic compared to visual representations. We have conducted two phenomenologically oriented comparative studies (applying the Repertory Grid and the Microphenomenological interview technique) to gather in-depth accounts of people's interpretation and experience of different representational modalities that included auditory, haptic and visual variations . Our findings show a rich characterization of these different representational modalities: our visually oriented representations engage through their familiarity, accuracy and easy interpretation, while our representations that stimulated auditory and haptic interpretation were experienced as more ambiguous, yet stimulated an engaging interpretation of data that involved the whole body. We describe and discuss in detail participants' processes of making sense and generating meaning using the modalities' unique characteristics, individually and as a group. Our research informs future research in the area of multimodal data representations from both a design and methodological perspective.Postprin

ShapeBots: Shape-changing Swarm Robots

We introduce shape-changing swarm robots. A swarm of self-transformable robots can both individually and collectively change their configuration to display information, actuate objects, act as tangible controllers, visualize data, and provide physical affordances. ShapeBots is a concept prototype of shape-changing swarm robots. Each robot can change its shape by leveraging small linear actuators that are thin (2.5 cm) and highly extendable (up to 20cm) in both horizontal and vertical directions. The modular design of each actuator enables various shapes and geometries of self-transformation. We illustrate potential application scenarios and discuss how this type of interface opens up possibilities for the future of ubiquitous and distributed shape-changing interfaces.Comment: UIST 201