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Coupled Marginal Fisher Analysis for Low-Resolution Face Recognition
Stephen Siena, Vishnu Naresh Boddeti, and B.V.K. Vijaya Kumar
Carnegie Mellon University, Electrical and Computer Engineering, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, USA 15213ssiena@andrew.cmu.edu
naresh@cmu.edu
kumar@ece.cmu.edu
Abstract. Many scenarios require that face recognition be performed at conditions that are not optimal. Traditional face recognition algorithms are not best suited for matching images captured at a low-resolution to a set of high-resolution gallery images. To perform matching between images of different resolutions, this work proposes a method of learning two sets of projections, one for high-resolution images and one for low-resolution images, based on local relationships in the data. Subsequent matching is done in a common subspace. Experiments show that our algorithm yields higher recognition rates than other similar methods.
LNCS 7584, p. 240 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012