Face recognition, a biometric technology that analyzes facial features to authenticate individuals’ identities, has various applications and implications across different fields. However, the advancement of technologies such as the Internet of Things has posed challenges for face recognition systems in terms of size, weight, cost, and privacy issues. In response to these challenges, some scholars have suggested a mask-based lensless face recognition system that captures facial images through a mask, eliminating the need for lenses. Nevertheless, the performance of lensless face recognition systems is limited by mask-based imaging, resulting in suboptimal results. To address this limitation, we propose a novel mask-based lensless face recognition system based on the Dual-Prior Face Restoration (DPFR) model. This model utilizes a dual-prior generator to create distinct facial priors that aid the Generative Adversarial Network (GAN) blocks in reconstructing both the global face structure and local face details. Extensive experiments have been carried out on the FlatCam Face Dataset (FCFD) captured using a lens camera and Flatcam lensless camera. The enhanced accuracy, precision, and True Accept Rate (TAR) performance metrics validate the effectiveness of the proposed mask-based lensless face recognition system.

人脸识别是一种分析面部特征以验证个人身份的生物识别技术，在不同领域具有多种应用和影响。然而，物联网等技术的进步在尺寸、重量、成本和隐私问题上给人脸识别系统带来了挑战。为了应对这些挑战，一些学者提出了一种基于面罩的无镜头人脸识别系统，该系统通过面罩捕捉面部图像，无需镜头。然而，无镜头人脸识别系统的性能受到基于掩模的成像的限制，导致结果欠佳。为了解决这一限制，我们提出了一种基于双先验人脸恢复 （DPFR） 模型的新型基于掩模的无晶状体人脸识别系统。该模型利用双先验生成器来创建不同的面部先验，以帮助生成对抗网络 （GAN） 模块重建全局面部结构和局部面部细节。已经对使用镜头相机和 Flatcam 无镜头相机拍摄的 FlatCam 人脸数据集 （FCFD） 进行了广泛的实验。增强的准确性、精度和真实接受率 （TAR） 性能指标验证了拟议的基于掩码的无镜头人脸识别系统的有效性。