Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Facial Expression Classification using Gabor and Log-Gabor Filters
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
A hybrid wavelet-based fingerprint matcher
Pattern Recognition
An automated palmprint recognition system
Image and Vision Computing
Texture-based palmprint retrieval using a layered search scheme for personal identification
IEEE Transactions on Multimedia
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information 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
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
An improved palmprint recognition system using iris features
Journal of Real-Time Image Processing
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Variations occurred on palmprint images degrade the performance of recognition. In this paper, we propose a novel approach to extract local invariant features using Gabor function, to handle the variations of rotation, translation and illumination, raised by the capturing device and the palm structure. The local invariant features can be obtained by dividing a Gabor filtered image into two-layered partitions and then calculating the differences of variance between each lower-layer sub-block and its resided upper-layer block (called local relative variance). The extracted features only reflect relations between local sub-blocks and its resided upper-layer block, so that the global disturbance occurred on palmprint images is counteracted. The effectiveness of the proposed method is demonstrated by the experimental results.