Fuzzy Directional Element Energy Feature (FDEEF) Based Palmprint Identification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Novel Palmprint Recognition Algorithm Based on PCA&FLD
ICDT '06 Proceedings of the international conference on Digital Telecommunications
A study of identical twins' palmprints for personal verification
Pattern Recognition
Texture Based Palmprint Identification Using DCT Features
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
A survey of palmprint recognition
Pattern Recognition
An automated palmprint recognition system
Image and Vision Computing
3-D Palmprint Recognition With Joint Line and Orientation Features
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A face and palmprint recognition approach based on discriminant DCT feature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Palm line extraction and matching for personal authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Probabilistic multiple face detection and tracking using entropy measures
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
In this paper, a multi-resolution feature extraction algorithm for palm-print recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several small spatial modules and the effect of modularization in terms of the entropy content of the palm-print images has been investigated. A palm-print recognition scheme is developed based on extracting dominant wavelet features from each of these local modules. In the selection of the dominant features, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, because of modularization of the palm-print image, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class separability of the extracted features. The effect of using different mother wavelets for the purpose of feature extraction has been also investigated. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.