Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisherpalms based palmprint recognition
Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Palmprint identification using feature-level fusion
Pattern Recognition
Personal authentication using multiple palmprint representation
Pattern Recognition
Face Recognition Based on Histogram of Modular Gabor Feature and Support Vector Machines
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Gabor texture in active appearance models
Neurocomputing
Palmprint verification using GridPCA for Gabor features
Proceedings of the Second Symposium on Information and Communication Technology
Palmprint verification based on 2D - Gabor wavelet and pulse-coupled neural network
Knowledge-Based Systems
GridLDA of Gabor wavelet features for palmprint identification
Proceedings of the Third Symposium on Information and Communication Technology
On-line fast palmprint identification based on adaptive lifting wavelet scheme
Knowledge-Based Systems
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In this paper, we propose a novel approach of Gabor feature-based (2D)^2PCA (GB(2D)^2PCA) for palmprint recognition. Three main steps are involved in the proposed GB(2D)^2PCA: (i) Gabor features of different scales and orientations are extracted by the convolution of Gabor filter bank and the original gray images; (ii) (2D)^2PCA is then applied for dimensionality reduction of the feature space in both row and column directions; and (iii) Euclidean distance and the nearest neighbor classifier are finally used for classification. The method is not only robust to illumination and rotation, but also efficient in feature matching. Experimental results demonstrate the effectiveness of our proposed GB(2D)^2PCA in both accuracy and speed.