Two-dimensional subspace classifiers for face recognition
Neurocomputing
Discriminant clustering embedding for face recognition with image sets
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Image-Set based face recognition using boosted global and local principal angles
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Margin preserving projection for image set based face recognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Face recognition in videos: a graph based modified kernel discriminant analysis
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Fusing cluster-centric feature similarities for face recognition in video sequences
Pattern Recognition Letters
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Recently, there has been a flurry of research on face recognition based on multiple images or shots from either a video sequence or an image set. This paper is also such an attempt in multiple-shot face recognition. Specifically, we propose a novel nonparametric method that first extracts discriminating local models via clustering. We apply a hierarchical distance-based clustering procedure according to some distance measure on the appearance manifold to cluster similar face images together. Based on the local models extracted, we then construct the intrapersonal and extrapersonal subspaces. Given a new test image, the angle between the projections of the image onto the two subspaces is used as a distance measure for classification. Since a test example contains multiple face images in multiple-shot face recognition, the final classification combines the classification decisions of all individual test images via a majority voting scheme. We compare our method empirically with some previous methods based on a database of video sequences of human faces, showing that out method significantly outperforms other methods.