A New Distance Criterion for Face Recognition Using Image Sets
A major face recognition paradigm involves recognizing a person from a set of images instead of from a single image. Often, the image sets are acquired from a video stream by a camera surveillance system, or a combination of images which can be non-contiguous and unordered. An effective algorithm that tackles this problem involves fitting low-dimensional linear subspaces across the image sets and using a linear subspace as an approximation for the particular face identity. Unavoidably, the individual frames in the image set will be corrupted by noise and there is a degree of uncertainty on how accurate the resultant subspace approximates the set. Furthermore, when we compare two linear subspaces, how much of the distance between them is due to inter-personal differences and how much is due to intra-personal variations contributed by noise? Here, we propose a new distance criterion, developed based on a matrix perturbation theorem, for comparing two image sets that takes into account the uncertainty of estimating a linear subspace from noise affected image sets.
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- 1.Lee, K., Ho, J., Yang, M., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: IEEE Conf. on Computer Vision and Pattern Recognition (2003)Google Scholar
- 2.Shakhnarovich, G., Fisher, J.W., Darrell, T.: Face recognition from long-term observations. In: European Conference on Computer Vision (2002)Google Scholar
- 4.Ariki, Y., Ishikawa, N.: Integration of face and speaker recognition by subspace method. In: Int. Conf. on Pattern Recognition, vol. 3, pp. 456–460 (1996)Google Scholar
- 5.Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Int. Conf. on Automatic Face and Gesture Recognition, pp. 318–323 (1998)Google Scholar
- 6.Fukui, K., Yamaguchi, O.: Face recognition using multi-viewpoint patterns for robot vision. In: 10th International Symposium of Robotics Research (2003)Google Scholar
- 7.Arandjelovic, O., Shakhnarovich, G., Fisher, J., Cipolla, R., Darrell, T.: Face recognition with image sets using manifold density divergence. In: CVPR (2005)Google Scholar
- 10.Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. John Hopkins University Press (1996)Google Scholar
- 12.Bishop, C.M.: Bayesian PCA. Advances in Neural Information Processing Systems 11, 382–388 (1998)Google Scholar
- 13.Bradski, G., Kaehler, A., Pisarevsky, V.: Learning-based computer vision with Intel’s open source computer vision library. Intel Technology Journal 9, 119–130 (2005)Google Scholar