Privacy Preserving Face Recognition with Lensless Camera
Chris Henry, M. Salman Asif, Zhu Li
Abstract
The widespread adoption of facial recognition technology is a global phenomenon. Facial recognition systems leverage upon image data containing faces. This poses serious threats to user privacy as the data is exposed to potential data breaches. In this paper, we propose a face recognition system that works without compromising user privacy. It utilizes data captured by FlatCam - a lensless camera. FlatCam captures the scene as a sensor measurement that is visually unintelligible. The proposed system preserves user privacy since it works directly on FlatCam’s sensor measurements without the need of FlatCam camera parameters which are required for pixel reconstruction. We propose a frequency domain deep learning solution that computes the DCT of the sensor field at multiple resolutions and organizes it into sub-bands before training a classification network with attention. The multi-resolution DCT subband representation leads to huge performance gains when compared to using the sensor measurement directly for training. Our proposed system was trained and tested on a real lensless camera dataset - the Flat-Cam Face dataset. Privacy of user is preserved during both training and testing. Experimental results demonstrate the effectiveness of our method.