差分三维面部识别:将 3D 技术添加到最新的 2D 方法中。

IF 20.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2020-07-01 Epub Date: 2020-04-13 DOI:10.1109/TPAMI.2020.2986951
J Matias Di Martino, Fernando Suzacq, Mauricio Delbracio, Qiang Qiu, Guillermo Sapiro
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引用次数: 0

摘要

主动照明是增强二维人脸识别能力的一个重要补充,它能使二维人脸识别在欺骗攻击和弱光条件下更加稳健。在本研究中,我们发现可以采用主动照明技术,利用三维特征增强最先进的二维人脸识别方法,同时绕过复杂的三维重建任务。其关键思路是在测试人脸上投射一个高空间频率图案,这样我们就能同时恢复真实的三维信息和标准的二维人脸图像。因此,最先进的二维人脸识别解决方案可以透明地应用,同时从输入图像的高频分量中提取互补的三维面部特征。在 ND-2006 数据集上的实验结果表明,所提出的想法可以显著提高人脸识别性能,并大大提高对欺骗攻击的鲁棒性。
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Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method.

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.

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来源期刊
CiteScore
28.40
自引率
3.00%
发文量
885
审稿时长
8.5 months
期刊介绍: The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.
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