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三维差分人脸识别:为您的最先进二维方法添加三维技术。

Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method.

出版信息

IEEE Trans Pattern Anal Mach Intell. 2020 Jul;42(7):1582-1593. doi: 10.1109/TPAMI.2020.2986951. Epub 2020 Apr 13.

Abstract

Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks.

摘要

主动照明是增强二维人脸识别并使其更具鲁棒性的重要补充,例如,抵御欺骗攻击和低光照条件。在本工作中,我们表明,可以采用主动照明来增强具有 3D 特征的最新二维人脸识别方法,同时绕过 3D 重建这一复杂任务。其关键思想是在测试人脸投射高空间频率图案,这使我们能够同时恢复真实的 3D 信息和标准的二维人脸图像。因此,可以透明地应用最新的二维人脸识别解决方案,同时从输入图像的高频分量中提取互补的 3D 人脸特征。在 ND-2006 数据集上的实验结果表明,所提出的方法可以显著提高人脸识别性能,并显著提高对欺骗攻击的鲁棒性。

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