发表机构
Rice University; Department of Electrical and Computer Engineering（莱斯大学; 电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对传统人脸识别系统的局限，引入无透镜面部数据集（LFD）。通过多种无透镜设备在不同环境下采集数据，包含现实世界无透镜面部数据与野外捕获。此数据集能有效捕捉不同设备的共享特征和伪像，推动无透镜人脸识别发展。
AI 中文摘要
人脸识别是一项广泛应用的计算机视觉任务，从日常智能手机生物识别到高风险安全系统。大多数人脸识别系统依赖传统相机，存在体积大、成本高和隐私保护有限等问题。无透镜相机作为替代方案出现，但其重建图像有伪像，且现有面部数据集未考虑无透镜图像中的伪像。为此，我们引入无透镜面部数据集（LFD），它包含21,080个无透镜原始测量、重建和标准面部图像，涵盖不同光照、角度和距离。我们的主要贡献包括：（1）现实世界的无透镜面部数据；（2）野外捕获；（3）多种无透镜设备。通过综合评估和分析，表明LFD有效捕捉了不同无透镜成像设备的共享特征和伪像，是推进无透镜人脸识别的宝贵数据集。
英文摘要
Face recognition is a ubiquitously used computer vision task that has a wide range of applications ranging from everyday smartphone biometrics to high-stakes security systems. Most face recognition systems rely on traditional cameras, which often suffer from limitations such as bulky form factors, high costs, and limited privacy protection. To address these limitations, lensless cameras have emerged as an alternative. Lensless cameras use thin optical encoders, enabling smaller size, lower cost, and greater design flexibility. These cameras are typically paired with reconstruction algorithms that convert raw captures into recognizable images. However, reconstructed images often contain artifacts, and the reconstruction methods struggle to generalize well to real-world conditions. Furthermore, existing face datasets do not account for the artifacts present in lensless images. To address this issue, we introduce the Lensless Face Dataset (LFD). LFD comprises 21,080 lensless raw measurements, reconstructions, and standard images of faces captured under diverse lighting, angle, and distance. Our key contributions are: (1) Real-world lensless face data: LFD focuses on capturing a diverse face dataset with varying levels of artifacts introduced under different environments; (2) In-the-wild captures: 4,976 images are captured in outdoor settings with varying intensities of natural light and different background patterns; (3) Multiple lensless devices: LFD includes face images collected from three different types of lensless cameras, each with a unique optical encoder. We use this hardware diversity to demonstrate generalization across different lensless cameras. Through comprehensive evaluations and analysis, we show that LFD effectively captures shared features and artifacts across different lensless imaging devices, making it a valuable dataset for advancing lensless face recognition.
Comments10 pages
