Computational complexity is a limiting factor for visualizing large-scale scientific data. Most approaches to render large datasets are focused on novel algorithms that leverage cutting-edge graphics hardware to provide users with an interactive experience. In this paper, we alternatively demonstrate foveated imaging which allows interactive exploration using low-cost hardware by tracking the gaze of a participant to drive the rendering quality of an image. Foveated imaging exploits the fact that the spatial resolution of the human visual system decreases dramatically away from the central point of gaze, allowing computational resources to be reserved for areas of importance. We demonstrate this approach using face tracking to identify the gaze point of the participant for both vector and volumetric datasets and evaluate our results by comparing against traditional techniques. In our evaluation, we found a significant increase in computational performance using our foveated imaging approach while maintaining high image quality in regions of visual attention.