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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning
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Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning

机译：欧氏和黎曼混合度量学习在野外对大型视频进行人脸识别

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Face recognition on large-scale video in the wild is becoming increasingly important due to the ubiquity of video data captured by surveillance cameras, handheld devices, Internet uploads, and other sources. By treating each video as one image set, set-based methods recently have made great success in the field of video-based face recognition. In the wild world, videos often contain extremely complex data variations and thus pose a big challenge of set modeling for set-based methods. In this paper, we propose a novel Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to fuse multiple statistics of image set. Specifically, we represent each image set simultaneously by mean, covariance matrix and Gaussian distribution, which generally complement each other in the aspect of set modeling. However, it is not trivial to fuse them since mean, covariance matrix and Gaussian model typically lie in multiple heterogeneous spaces equipped with Euclidean or Riemannian metric. Therefore, we first implicitly map the original statistics into high dimensional Hilbert spaces by exploiting Euclidean and Riemannian kernels. With a LogDet divergence based objective function, the hybrid kernels are then fused by our hybrid metric learning framework, which can efficiently perform the fusing procedure on large-scale videos. The proposed method is evaluated on four public and challenging large-scale video face datasets. Extensive experimental results demonstrate that our method has a clear superiority over the state-of-the-art set-based methods for large-scale video-based face recognition. (C) 2015 Elsevier Ltd. All rights reserved.
机译：由于监控摄像机，手持设备，Internet上传和其他来源捕获的视频数据无处不在，因此在野外对大型视频进行人脸识别变得越来越重要。通过将每个视频视为一个图像集，基于集合的方法最近在基于视频的面部识别领域取得了巨大的成功。在野外，视频通常包含极其复杂的数据变化，因此对基于集合的方法进行集合建模提出了很大的挑战。在本文中，我们提出了一种新颖的混合欧氏和黎曼度量学习（HERML）方法，以融合图像集的多个统计信息。具体来说，我们通过均值，协方差矩阵和高斯分布同时表示每个图像集，它们在集合建模方面通常相互补充。但是，融合它们并非易事，因为均值，协方差矩阵和高斯模型通常位于配备欧几里德或黎曼度量的多个异构空间中。因此，我们首先通过利用欧几里得核和黎曼核将隐含的原始统计信息映射到高维希尔伯特空间。使用基于LogDet发散的目标函数，然后通过我们的混合度量学习框架将混合内核融合，该框架可以有效地对大型视频执行融合过程。该方法在四个公开且具有挑战性的大规模视频人脸数据集上进行了评估。大量的实验结果表明，对于基于视频的大规模人脸识别，我们的方法比基于最新技术的集合方法具有明显优势。 （C）2015 Elsevier Ltd.保留所有权利。

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