Progress in any technological area requires distinct breakthrough ideas. In the field of imaging, lensless imaging technology is a disruptive concept that allows cameras to continue getting thinner and cheaper. The FlatCam lensless imaging system demonstrates this by replacing the thick and expensive lens of a conventional camera with a thin and cheap aperture mask and a reconstruction algorithm. Indeed, such a design allows recognizable image capture, albeit with much lower resolution and greater noise than conventional lens-based cameras.

The true disruptive ability of FlatCam in society is its potential to fuel a machine's capability of obtaining a wealth of information from the world via images, a common step in the pipeline of machine intelligence. In this work, I rigorously demonstrate and evaluate performing face detection and verification, two such intelligence tasks, with FlatCam images.

To perform face detection and verification, I propose and adapt basic deep learning techniques to handle the resolution, noise, and artifacts inherent with the FlatCam. I show with common evaluation protocols that there is only a small decrease in accuracy when using FlatCam images compared to the original lens-based images. Furthermore, I describe the construction of a face dataset captured with a FlatCam prototype containing 23,368 lensless camera images of 92 subjects in a range of different operating conditions. Further evaluating face verification on this dataset verifies the FlatCam's potential for performing inference tasks in real-world deployment.