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
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Does EigenPalm work? A System and Evaluation Perspective
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Palmprint identification using feature-level fusion
Pattern Recognition
Personal recognition based on an image of the palmar surface of the hand
Pattern Recognition
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Personal recognition using hand shape and texture
IEEE Transactions on Image Processing
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Palmprint authentication using a symbolic representation of images
Image and Vision Computing
Short Communication: A novel local preserving projection scheme for use with face recognition
Expert Systems with Applications: An International Journal
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Stockwell transform based palm-print recognition
Applied Soft Computing
Palmprint verification using GridPCA for Gabor features
Proceedings of the Second Symposium on Information and Communication Technology
An improved palmprint recognition system using iris features
Journal of Real-Time Image Processing
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Recently, two-dimensional locality preserving projections (2DLPP) was proposed to extract features directly from image matrices based on locality preserving criterion. Though 2DLPP has been applied in many domains including face and palmprint recognition, it still has several disadvantages: the nearest-neighbor graph fails to model the intrinsic manifold structure inside the image; large dimensionality training space affects the calculation efficiency; and too many coefficients are needed for image representation. These problems inspire us to propose an improved 2DLPP (I2DLPP) for recognition in this paper. The modifications of the proposed I2DLPP mainly focus on two aspects: firstly, the nearest-neighbor graph is constructed in which each node corresponds to a column inside the matrix, instead of the whole image, to better model the intrinsic manifold structure; secondly, 2DPCA is implemented in the row direction prior to 2DLPP in the column direction, to reduce the calculation complexity and the final feature dimensions. By using the proposed I2DLPP, we achieve a better recognition performance in both accuracy and speed. Furthermore, owing to the robustness of Gabor filter against variations, the improved 2DLPP based on the Gabor features (I2DLPPG) can further enhance the recognition rate. Experimental results on the two palmprint databases of our lab demonstrate the effectiveness of the proposed method.