Image Set based Collaborative Representation for Face Recognition
This addresses face recognition challenges in scenarios with multiple images per person, offering improved accuracy and speed for applications like surveillance or biometrics, though it is incremental as it extends existing collaborative representation techniques.
The paper tackles the problem of image set based face recognition by proposing a collaborative representation method that models the query set as a convex or regularized hull and represents it over all gallery sets, achieving superior recognition rates and efficiency compared to state-of-the-art methods on benchmark databases.
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.