High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Competitive Coding Scheme for Palmprint Verification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Palmprint identification using feature-level fusion
Pattern Recognition
Minutiae feature analysis for infrared hand vein pattern biometrics
Pattern Recognition
A Performance Evaluation of Shape and Texture Based Methods for Vein Recognition
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Personal authentication using hand vein triangulation and knuckle shape
IEEE Transactions on Image Processing
Gaussian-based edge-detection methods-a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Biometric verification using thermal images of palm-dorsa vein patterns
IEEE Transactions on Circuits and Systems for Video Technology
Human Identification Using Palm-Vein Images
IEEE Transactions on Information Forensics and Security
Palm vein recognition using adaptive Gabor filter
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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With the increasing needs in security systems, vein recognition is one of the important and reliable solutions of identity security for biometrics-based identification systems. The obvious and stable line-feature-based approach can be used to clearly describe a palm vein patterns for personal identification. In this paper, a directional filter bank involving different orientations is designed to extract the vein pattern and the minimum directional code (MDC) is employed to encode the line-based vein features in binary code. In addition, there are many non vein pixels in the vein image and those pixels are unmeaning for vein recognition. To improve the accuracy, the non-vein pixels are detected by evaluating the directional filtering magnitude (DFM) and considered the non-orientation code. A total of 5120 palm vein images from 256 persons are used to verify the validity of the proposed palm vein recognition approach. High accuracies (99%) and low equal error rate (0.54%) obtained by the proposed method show that our proposed approach is feasible and effective for palm vein recognition.