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Regularized Discriminant Analysis for Holistic Human Activity Recognition
January/February 2012 (vol. 27 no. 1)
pp. 21-31
Bappaditya Mandal, Institute for Infocomm Research, A*STAR
How-Lung Eng, Institute for Infocomm Research, A*STAR

A holistic or appearance-based eigenfeature regularization methodology based on a three-parameter eigenmodel improves computer recognition of human activity.

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1. T. Moeslund, A. Hiltron, and V. Kruger, "A Survey of Advances in Vision-based Human Motion Capture and Analysis," Computer Vision and Image Understanding, vol. 104, no. 2, 2006, pp. 90–126.
2. P.N. Belhumeur, J.P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, 1997, pp. 711–720.
3. X.D. Jiang, B. Mandal, and A. Kot, "Eigenfeature Regularization and Extraction in Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 3, 2008, pp. 383–394.
4. X. Zhuang and D. Dai, "Improved Discriminant Analysis for High-Dimensional Data and Its Application to Face Recognition," Pattern Recognition, vol. 40, 2007, pp. 1570–1578.

Index Terms:
activity recognition, linear discriminant analysis, feature extraction, subspace methods
Citation:
Bappaditya Mandal, How-Lung Eng, "Regularized Discriminant Analysis for Holistic Human Activity Recognition," IEEE Intelligent Systems, vol. 27, no. 1, pp. 21-31, Jan.-Feb. 2012, doi:10.1109/MIS.2010.114
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