A Novel Approach for Automatic Palmprint Recognition
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Palmprint Recognition by Applying Wavelet Subband Representation and Kernel PCA
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Personal Identification Using Palmprint and Contourlet Transform
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A feature level multimodal approach for palmprint identification using directional subband energies
Journal of Network and Computer Applications
A wavelet-based dominant feature extraction algorithm for palm-print recognition
Digital Signal Processing
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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 technique based on second-order information in the sense of mean-square error. FLD is one of the most popular linear classification techniques for feature detection. In this paper, we proposed a novel method based on traditional PCA&FLD method. In this method, PCA is not only used for reducing dimension, the PCA feature is also used again to make a fusion with FLD feature in recognition stage. We imply our method to PolyU Palmprint database and the experiment result shows that the novel method is better.