NVESD is currently researching relationships between facial thermal signatures and underlying physiological stress.
This paper describes some of the initial findings of a study involving participants watching emotion-eliciting video clips
while being monitored by a high-resolution, Mid-Wave InfraRed (MWIR), cooled sensor system. Both global and region-specific temperature profiles of the face were obtained for each participant from thermal videos recorded during the experiment. Changes in the global temperature patterns of each participant were shown to be consistent with changes in the visual and auditory stimuli. In some cases, there were inconsistencies in thermal trends among local facial regions
of individual participants. This indicates the possibility that different regions may exhibit different thermal patterns in
response to a stimulus.
The Pacific Northwest National Laboratory is involved in the design and development of algorithms to improve feature identification and detection using multisensor imagery. This research is funded jointly by the National Imagery and Mapping Agency (NIMA) and the U.S. Department of Energy. A process has been designed that exploits the spatial discontinuities in a scene as revealed by the reflectance variation in a given frequency. We believe that by mapping the discontinuities in a scene, man-made objects can be better distinguished from natural objects. The process involves the generation of a texture map for each of the multisensor data sets; this facilitates the fusion of data from different sources with different physical characteristics. The advantage of this approach is that texture seems to reduce image data to a common base. This common base becomes important when using data of variable quality, resolution, and geometry. Texture analysis has applicability to a wide variety of feature identification and extraction applications. This paper focus on demonstrating how the classification of texture maps derived from multisensor imagery can be used to automatically extract major roads from multisensor imagery, a requirement from NIMA under its comprehensive and integrated geospatial information generation strategy. Automatic/assisted road extraction is a particularly challenging task given the need for global coverage, accurate positioning, and sophisticated attribution.