Lensless facial image identification through joint learning of reconstruction and recognition

Mask-based lensless camera systems, replacing traditional lenses with a thin mask and sensor, offer unique imaging capabilities and enhanced privacy through optical encoding. This paper presents a facial identification system based on lensless camera images. The main contribution of this study is a framework that jointly learns lensless image reconstruction and recognition tasks using a dual-stream neural network. Unlike existing methods that focus solely on object recognition from lensless images, the proposed method leverages reconstructed image knowledge to improve face identification accuracy. By activating only the facial identification stream during inference, we ensure privacy protection while enhancing identification performance. Experiments demonstrate that our method outperforms state-of-the-art lensless facial identification methods.