  Efficient and accurate joint representation of a collection of images, that
belong to the same class, is a major research challenge for practical image set
classification. Existing methods either make prior assumptions about the data
structure, or perform heavy computations to learn structure from the data
itself. In this paper, we propose an efficient image set representation that
does not make any prior assumptions about the structure of the underlying data.
We learn the non-linear structure of image sets with Deep Extreme Learning
Machines (DELM) that are very efficient and generalize well even on a limited
number of training samples. Extensive experiments on a broad range of public
datasets for image set classification (Honda/UCSD, CMU Mobo, YouTube
Celebrities, Celebrity-1000, ETH-80) show that the proposed algorithm
consistently outperforms state-of-the-art image set classification methods both
in terms of speed and accuracy.
