Pseudo RGB-D Face Recognition
Pseudo RGB-D Face Recognition
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DOI：
10.1109/jsen.2022.3197235
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发表时间：
2022-11-15
影响因子：
4.3
通讯作者：
Goncalves, Nuno
中科院分区：
文献类型：
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作者：
Jin, Bo;Cruz, Leandro;Goncalves, Nuno
在过去的十年中，低成本RGB-D传感器的进步和普及使我们能够获取物体的深度信息。因此，研究人员开始通过使用这些传感器捕获RGB-D人脸图像来解决人脸识别问题。到目前为止，由于隐私政策的限制，获取人脸的深度并不容易，RGB人脸图像仍然较为常见。因此，直接从相应的RGB图像获得深度图可能有助于提高后续人脸处理任务（例如人脸识别）的性能。智能生物可以利用大量的经验，从二维平面场景中获取三维空间信息。正是机器学习方法，解决了这些问题，可以教计算机通过训练生成正确的答案。为了用生成的伪深度图代替深度传感器，在本文中，我们提出了一个伪RGB-D人脸识别框架，并提供了数据驱动的方法来从2D人脸图像生成深度图。特别地，我们设计并实现了一个名为“D+GAN”的生成式对抗网络模型来执行具有人脸属性的多条件图像到图像的翻译。通过这种方法，我们验证了伪RGB-D人脸识别与各种数据集上的实验。在图像融合技术，特别是非下采样剪切波变换（NSST）的配合下，人脸识别的准确率得到了显著的提高。
In the last decade, advances and popularity of low-cost RGB-D sensors have enabled us to acquire depth information of objects. Consequently, researchers began to solve face recognition problems by capturing RGB-D face images using these sensors. Until now, it is not easy to acquire the depth of human faces because of limitations imposed by privacy policies, and RGB face images are still more common. Therefore, obtaining the depth map directly from the corresponding RGB image could be helpful to improve the performance of subsequent face processing tasks, such as face recognition. Intelligent creatures can use a large amount of experience to obtain 3D spatial information only from 2D plane scenes. It is machine learning methodology, which is to solve such problems, that can teach computers to generate correct answers by training. To replace the depth sensors by generated pseudo-depth maps, in this article, we propose a pseudo RGB-D face recognition framework and provide data-driven ways to generate the depth maps from 2D face images. Specially we design and implement a generative adversarial network model named "D+GAN" to perform the multiconditional image-to-image translation with face attributes. By this means, we validate the pseudo RGB-D face recognition with experiments on various datasets. With the cooperation of image fusion technologies, especially non-subsampled shearlet transform (NSST), the accuracy of face recognition has been significantly improved.