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.

本文介绍了一种基于图像集的协作表示和分类方法，通过建模查询集作为一个凸或正则化壳，在所有的图库集合上协作表示该外壳，进而计算分类时查询集与每个图库集之间的距离，该模型自然有效地将基于图像的协作表示扩展为基于图像集的，则在各种集合大小下远超同类方法的ISFR方法 的识别率和效率。

基于图像集合的协同表示用于人脸识别