Independent component analysis: algorithms and applications
Neural Networks
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Image Reconstruction Using a Modified Sparse Coding Technique
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Palmprint Verification Using Circular Gabor Filter
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
An automated palmprint recognition system
Image and Vision Computing
Kernel sparse representation based classification
Neurocomputing
On single image scale-up using sparse-representations
Proceedings of the 7th international conference on Curves and Surfaces
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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To capture the nonlinear similarity of palmprint image features, a new palmprint recognition method utilizing the kernel trick based sparse representation (KSR) algorithm is proposed in this paper. KSR is in fact an essential sparse coding technique in a high dimensional feature space mapped by implicit mapping function, and it can efficiently reduce the feature quantization error and enhance the sparse coding performance. Here, to reduce the time of sparse coding, the fast sparse coding (FSC) is used in coding stage. FSC solves the L1 - regularized least squares problem and the L2 -constrained least squares problem by iterative method, and it has a faster convergence speed than the existing SC model. In test, the PolyU palmprint database used widely in palmprint recognition research is selected. Using the Gauss kernel function and considering different feature dimensions, the task of palmprint recognition obtained by KSR can be successfully implemented. Furthermore, compared our method with general SR and SC under different feature dimensions, the simulation results show further that this method proposed by us is indeed efficient in application.