Large Margin Image Set Representation and Classification

TL;DR

This paper introduces a new image set classification method that maximizes the margin between classes using affine hull models and EM optimization, leading to improved face recognition performance.

Contribution

It proposes a novel margin-based image set representation and classification framework utilizing affine hull models and an EM-based optimization strategy.

Findings

Outperforms state-of-the-art methods in face recognition accuracy.

Demonstrates significant improvements in classification efficiency.

Effective in handling video-sequence-based face recognition tasks.

Abstract

In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation -maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.