Personal authentication using hand images
Pattern Recognition Letters
Wavelet decomposition tree selection for palm and face authentication
Pattern Recognition Letters
A survey of palmprint recognition
Pattern Recognition
Palmprint Verification Using Circular Gabor Filter
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Automated flexion crease identification using internal image seams
Pattern Recognition
An automated palmprint recognition system
Image and Vision Computing
Palmprint verification using hierarchical decomposition
Pattern Recognition
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Stockwell transform based palm-print recognition
Applied Soft Computing
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Palmprint recognition using polynomial neural network
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Palmprint based recognition system using phase-difference information
Future Generation Computer Systems
A wavelet-based dominant feature extraction algorithm for palm-print recognition
Digital Signal Processing
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Palmprint is a novel biometric method to identify a person. Generally, there are two types of features in palmprint, i.e. structural features and statistical features. Structural features, such as lines, can characterize a palm exactly, but are difficult to be extracted and represented.Contrarily, statistical features can be extracted and represented easily, but are unable to reflect the structural information of a palmprint. The fact that the principal features of both Chinese character and palmprint are lines motivates us to try some methods of Chinese character recognition to identify palmprint. In this paper, we use the idea of an efficient Chinese character recognition method, directional element feature (DEF), to define a novel palm-print feature, named fuzzy directional element energy feature (FDEEF) which is a statistical feature containing some line structural information about palmprints. It can be extracted and represented easily and, at the same time, has a strong ability to distinguish palms. Two other low-dimensional features: global fuzzy directional element energy feature (GFDEEF) and block edge energy feature (BEEF) are also derived from FDEEF in this paper. The experimental results demonstrate the power of this method.