On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A modified Gabor filter design method for fingerprint image enhancement
Pattern Recognition Letters
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Vision and Applications
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale multiplication in odd Gabor transform domain for edge detection
Journal of Visual Communication and Image Representation
ICECT '09 Proceedings of the 2009 International Conference on Electronic Computer Technology
Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles
IEICE - Transactions on Information and Systems
Vein segmentation in infrared images using compound enhancing and crisp clustering
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
Towards finger-vein image restoration and enhancement for finger-vein recognition
Information Sciences: an International Journal
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Finger-vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. The advantages of finger vein over traditional biometrics (e.g. face, fingerprint, and iris) lie in low-risk forgery, noninvasiveness, and noncontact. This paper here presents a new method of personal identification based on finger-vein recognition. First, a stable region representing finger-vein network is cropped from the image plane of an imaging sensor. A bank of Gabor filters is then used to exploit the finger-vein characteristics at different orientations and scales. Based on the filtered image, both local and global finger-vein features are extracted to construct a finger-vein code (FVCode). Finally, finger-vein recognition is implemented using the cosine similarity measure classifier, and a fusion scheme in decision level is adopted to improve the reliability of identification. Experimental results show that the proposed method exhibit an exciting performance in personal identification.