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
Robust Biometric System Using Palmprint for Personal Verification
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Kernel Principal Component Analysis of Gabor Features for Palmprint Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Learning Gabor magnitude features for palmprint recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Analog Integrated Circuits and Signal Processing
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
An improved palmprint recognition system using iris features
Journal of Real-Time Image Processing
Selection of discriminative sub-regions for palmprint recognition
Multimedia Tools and Applications
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In this paper, a method for palmprint recognition, kernel Fisher discriminant analysis (KFDA), is proposed. The method introduces KFDA to represent palmprint features for palmprint recognition. In the paper, a device without fixed peg is developed to capture palmprint images. Because the movement, the rotation and the stretching of hands are uncontrollable, the features extracted from these palmprint images have a little nonlinearity. Classic linear feature extraction approaches, such as PCA and FLDA, only take the 2-order statistics among palmprint image pixels into account, and are not sensitive to higher order statistics of data. Therefore, KFDA is used to extract higher order relations among palmprint images for future recognition. The experiment results denote that KFDA have a better performance than eigenpalms and fisherpalms, especially in case of using a small quantity of training samples.