A majority of the image set based face recognition methods use a generatively learned model for each person that is learned independently by ignoring the other persons in the gallery set. In contrast to these methods, this paper introduces a novel method that searches for discriminative convex models that best fit to an individual’s face images but at the same time are as far as possible from the images of other persons in the gallery. We learn discriminative convex models for both affine and convex hulls of image sets. During testing, distances from the query set images to these models are computed efficiently by using simple matrix multiplications, and the query set is assigned to the person in the gallery whose image set is closest to the query images. The proposed method significantly outperforms other methods using generatively learned convex models in terms of both accuracy and testing time, and achieves the state-of-the-art results on six of the eight tested datasets. Especially, the accuracy improvement is significant on the challenging PaSC, COX, IJB-C and ESOGU video datasets.

中文翻译：

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使用判别学习的凸模型进行基于视频的人脸识别

大多数基于图像集的人脸识别方法对每个人使用生成学习模型，该模型通过忽略图库集中的其他人而独立学习。与这些方法相比，本文介绍了一种新方法，该方法搜索最适合个人面部图像但同时尽可能远离图库中其他人图像的判别凸模型。我们为图像集的仿射和凸包学习判别凸模型。在测试过程中，通过使用简单的矩阵乘法有效地计算从查询集图像到这些模型的距离，并将查询集分配给图库中图像集最接近查询图像的人。所提出的方法在准确性和测试时间方面明显优于使用生成学习凸模型的其他方法，并在八个测试数据集中的六个数据集中取得了最先进的结果。特别是，在具有挑战性的 PaSC、COX、IJB-C 和 ESOGU 视频数据集上，准确率的提高是显着的。