Abstract
A new method of feature extraction, based on feature fusion, is proposed in this paper according to the idea of canonical correlation analysis (CCA). At first, the theory framework of CCA used in pattern recognition and its reasonable description are discussed. The process can be explained as follows: extract two groups of feature vectors with the same pattern; establish the correlation criterion function between the two groups of feature vectors; and extract their canonical correlation features to form effective discriminant vector for recognition. Then, the problem of canonical projection vectors is solved when two total scatter matrixes are singular, such that it fits for the case of high-dimensional space and small sample size, in this sense, the applicable range of CCA is extended. At last, the inherent essence of this method used in recognition is analyzed further in theory. Experimental results on Concordia University CENPARMI database of handwritten Arabic numerals and Yale face database show that recognition rate is far higher than that of the algorithm adopting single feature or the existing fusion algorithm.
Published Version
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