Appearance-Based Gaze Estimation in the Wild

Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild

Collaboration Development through Interactive Learning between Human and Robot

In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions

Spin melting and refreezing driven by uniaxial compression on a dipolar hexagonal plate

We investigate freezing characteristics of a finite dipolar hexagonal plate by the Monte Carlo simulation. The hexagonal plate is cut out from a piled triangular lattice of three layers with FCC-like (ABCABC) stacking structure. In the present study an annealing simulation is performed for the dipolar plate uniaxially compressed in the direction of layer-piling. We find spin melting and refreezing driven by the uniaxial compression. Each of the melting and refreezing corresponds one-to-one with a change of the ground states induced by compression. The freezing temperatures of the ground-state orders differ significantly from each other, which gives rise to the spin melting and refreezing of the present interest. We argue that these phenomena are originated by a finite size effect combined with peculiar anisotropic nature of the dipole-dipole interaction.Comment: Proceedings of the Highly Frustrated Magnetism (HFM2006) conference. To appear in a special issue of J. Phys. Condens. Matte