Image Set based Collaborative Representation for Face Recognition

TL;DR

This paper introduces a novel collaborative representation method for image set based face recognition, modeling query sets as convex hulls and representing them over gallery sets to improve recognition accuracy and efficiency.

Contribution

It extends collaborative representation from individual images to image sets by modeling query sets as convex hulls and collaboratively representing them over gallery sets, enhancing recognition performance.

Findings

Outperforms state-of-the-art ISFR methods in recognition rate

Demonstrates improved efficiency in face recognition tasks

Effective across different set sizes

Abstract

With the rapid development of digital imaging and communication technologies, image set based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to effectively and efficiently represent the query face image set by using the gallery face image sets. The set-to-set distance based methods ignore the relationship between gallery sets, while representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set based collaborative representation and classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representation coefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally and effectively extends the image based collaborative representation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.