Hybrid Approach for Palmprint Recognition Using Compound Features
As patterns in a palmprint have abundance of invariance, the inter-class and intra-class variability of these features makes it difficult for just one set of features to capture this variability. This inspires us to propose a hybrid feature extraction and fusion approach for palmprint recognition based on texture information available in the palm. Scale, shift and rotation (Affine) invariance, good directional sensitivity properties of Dual-tree Complex Wavelets makes it a choice to capture texture features at global level. Local Binary Pattern on the other hand being gray-scale and rotation invariant, captures local fine textures effectively. These local features are sensitive to position and orientation of the palm image. Canonical Correlation Analysis is used to combine the features at the descriptor level which ensures that the information captured from both the features are maximally correlated and eliminate the redundant information giving a more compact representation. Experimental results demonstrate an accuracy of 97.2% at an EER of 3.2% on CASIA palmprint database.
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