A survey of palmprint recognition
Pattern Recognition
Pose Invariant Palmprint Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
A novel feature selection based semi-supervised method for image classification
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Palmprint identification using PCA algorithm and hierarchical neural network
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Hi-index | 0.00 |
Various techniques in analyzing palmprint have been proposed but to the best of our knowledge, none has been studied on the selection and division of the region-of-interest (ROI). Previous methods were always applied only to a fixed size square region chosen as the central part of the palm, which were then divided into square blocks for extraction of local features. In this paper, we proposed a new method in locating and segmenting the ROI for palmprint analysis, where the selected region varies with the size of the palm. Instead of square blocks, the region is divided into sectors of elliptical half-rings, which are less affected by misalignment due to rotational error. More importantly, our arrangement of the feature vectors ensures that only features extracted from the same spatial region of two aligned palms will be compared with each other. Encouraging results obtained favor the use of this method in the future development of palmprint analysis techniques.