We present a simple yet compact and discriminative representation for image sets which can efficiently be used for image-set based object classification. For each image-set we compute a global covariance matrix which captures correlated variations in all image-set dimensions. Without loss of information, we compact the covariance matrix into a lower triangular matrix by using Cholesky decomposition. While preserving discrimination capability of the representation, we obtain further compression by applying Multiple Discriminant Analysis. As a result, we are able to represent image sets containing N samples each of dimensionality d by a single vector whose dimensionality is << N d. We apply the proposed representation to various biometric applications such as image-set based face recognition and person identification using image-sets of periocular regions. To show that our representation is generic, we also report results for image-set based object categorization. We observe improved accuracy and significant speedup over the current state-of-the-art techniques on standard datasets.
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机译：我们为图像集提供了一个简单而紧凑且辨别的表示，可以有效地用于基于图像集的对象分类。对于每个图像集，我们计算全局协方差矩阵，该矩阵捕获所有图像集尺寸的相关变化。不损失信息，我们通过使用Cholesky分解将协方差矩阵紧凑到较低的三角矩阵。在保持表示的辨别能力的同时，通过应用多次判别分析，我们获得进一步的压缩。结果，我们能够表示包含N个样本的图像集，这些样品D维度D的每一个矢量，其维度为 n d。我们将建议的表示应用于各种生物识别应用，例如基于图像集的面部识别和人身份识别，使用围面区域的图像集。为了表明我们的表示是通用的，我们还向基于图像集的对象分类报告结果。我们在标准数据集上观察到最新的最先进技术的提高准确性和显着加速。
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