Spatiotemporal salient points for visual recognition of human actions

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We introduce an appropriate distance metric between two collections of spatiotemporal salient points that is based on the Chamfer distance and an iterative linear time warping technique that deals with time expansion or time compression issues. We propose a classification scheme that is based on Relevance Vector Machines and on the proposed distance measure. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises

4DFAB: a large scale 4D facial expression database for biometric applications

The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases. To this end, we propose 4DFAB, a new large scale database of dynamic high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour. In this paper, we conduct several experiments and demonstrate the usefulness of the database for various applications. The database will be made publicly available for research purposes

Active nonrigid ICP algorithm

© 2015 IEEE.The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 15-20 years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region

Discrete vs. Discretized Growth Discretized Fabrication of Geometries Generated with Cellular Growth Simulations

Discrete computational growth simulations, such as Cellular Automata of Diffusion Limited Aggregation, appear often to be difficult to use for architectural design as their geometric outcomes tend to be difficult to control. On the contrary, free-form growth simulations such as Differential Growth or cell-based growth algorithms produce highly complex geometries that are difficult to construct at a larger scale. We, therefore, propose a methodology of discretized free-form Cellular Growth algorithms in order to utilize the emerging qualities of growth simulations for a feasible architectural design. The methodology has been tested within the framework of a workshop and resulted in the efficient construction of a large physical prototype