In this paper, a new metric learning algorithm is proposed to improve face verification and person re-identification in RGB images by learning from RGB and Depth (RGB-D) training images. We address this problem by formulating it as a Learning Using Privileged Information problem, in which the additional depth images associated with the RGB training images are not available for the testing process. Based on the large margin nearest neighbor (LMNN) classification framework, we propose an effective metric learning method called large margin nearest neighbor classification with privileged information (LMNN+) by incorporating depth information to improve decision function learning in the training process. Specifically, two distance metrics based on visual features as well as depth features are jointly learned by minimizing the triplet loss in which the within-class difference is minimized, while the between-class difference is maximized. The distances in the depth feature space can be utilized to guide the training process in the visual feature space. In addition, we propose an efficient optimization method which can handle billions of constraints in the optimization problem of LMNN+. The comprehensive experiments on the EUROCOM data set, the CurtinFaces data set as well as the BIWI RGBD-ID data set demonstrate the effectiveness of our algorithm for face verification and person re-identification by leveraging privileged information.

中文翻译：

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用于生物识别应用的具有特权信息的大边距最近邻分类

在本文中，提出了一种新的度量学习算法，通过从 RGB 和深度 (RGB-D) 训练图像中学习来改进 RGB 图像中的人脸验证和人员重新识别。我们通过将其制定为使用特权信息学习问题来解决这个问题，其中与 RGB 训练图像相关的附加深度图像不可用于测试过程。基于大边缘最近邻（LMNN）分类框架，我们提出了一种有效的度量学习方法，称为具有特权信息的大边缘最近邻分类（LMNN+），通过在训练过程中结合深度信息来改进决策函数学习。具体来说，通过最小化三元组损失来联合学习基于视觉特征和深度特征的两个距离度量，其中类内差异最小化，而类间差异最大化。深度特征空间中的距离可用于指导视觉特征空间中的训练过程。此外，我们提出了一种有效的优化方法，可以处理 LMNN+ 优化问题中的数十亿个约束。EUROCOM 数据集、CurtinFaces 数据集以及 BIWI RGBD-ID 数据集的综合实验证明了我们的算法通过利用特权信息进行人脸验证和人员重新识别的有效性。深度特征空间中的距离可用于指导视觉特征空间中的训练过程。此外，我们提出了一种有效的优化方法，可以处理 LMNN+ 优化问题中的数十亿个约束。EUROCOM 数据集、CurtinFaces 数据集以及 BIWI RGBD-ID 数据集的综合实验证明了我们的算法通过利用特权信息进行人脸验证和人员重新识别的有效性。深度特征空间中的距离可用于指导视觉特征空间中的训练过程。此外，我们提出了一种有效的优化方法，可以处理 LMNN+ 优化问题中的数十亿个约束。EUROCOM 数据集、CurtinFaces 数据集以及 BIWI RGBD-ID 数据集的综合实验证明了我们的算法通过利用特权信息进行人脸验证和人员重新识别的有效性。