Lensless cameras, innovatively replacing traditional lenses for ultra-thin,
flat optics, encode light directly onto sensors, producing images that are not
immediately recognizable. This compact, lightweight, and cost-effective imaging
solution offers inherent privacy advantages, making it attractive for
privacy-sensitive applications like face verification. Typical lensless face
verification adopts a two-stage process of reconstruction followed by
verification, incurring privacy risks from reconstructed faces and high
computational costs. This paper presents an end-to-end optimization approach
for privacy-preserving face verification directly on encoded lensless captures,
ensuring that the entire software pipeline remains encoded with no visible
faces as intermediate results. To achieve this, we propose several techniques
to address unique challenges from the lensless setup which precludes
traditional face detection and alignment. Specifically, we propose a face
center alignment scheme, an augmentation curriculum to build robustness against
variations, and a knowledge distillation method to smooth optimization and
enhance performance. Evaluations under both simulation and real environment
demonstrate our method outperforms two-stage lensless verification while
enhancing privacy and efficiency. Project website:
\url{lenslessface.github.io}.

使用不需要镜头的相机对编码无镜头捕捉的隐私保护人脸验证进行端到端优化，确保整个软件流程在编码过程中没有可见人脸的中间结果，通过面部中心对齐方案、增强训练和知识蒸馏等技术解决了不同于传统人脸检测和对齐的不同挑战，评估结果显示该方法优于两阶段无镜头验证，同时提升了隐私保护和效率。