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Palmprint recognition received many researchers’ attention because of it’s low resolution and cheap devices. As other biometrics, algebraic feature is the prevailing method for palmprint recognition. PCA (principal component analysis) is one prevailing algebraic transformation. It has been a successful feature detection method for pattern recognition. It deals with image vector whose dimension is...
PCA (principal component analysis) is a successful feature detection method for pattern recognition. It is the optimal dimension compression technique based on second-order information, in the sense of mean-square error. It deals with image vector whose dimension is usually high. 2DPCA is a novel PCA method for image matrix, and it can calculate the covariance matrix more precise. In this paper we...
Recently palmprint recognition received many researchers' attention because of it 's low resolution and cheap devices. As other features recognition, algebraic feature is the prevailing method for palmprint recognition. PCA and FLD features belong to this feature, and they all have successfully been used for palmprint recognition. PCA (principal component analysis) is the optimal dimension compression...
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