On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study of Zernike Moments
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Personal Identification Utilizing Finger Surface Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Exploiting finger surface as a biometric identifier
Exploiting finger surface as a biometric identifier
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Palmprint verification based on robust line orientation code
Pattern Recognition
A multi-matcher system based on knuckle-based features
Neural Computing and Applications
Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Finger surface as a biometric identifier
Computer Vision and Image Understanding
Personal authentication using finger knuckle surface
IEEE Transactions on Information Forensics and Security
Human identification using Knucklecodes
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Online finger-knuckle-print verification for personal authentication
Pattern Recognition
Finger-knuckle-print: a new biometric identifier
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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The aim of this paper is to study the effect of feature level fusion of multi instances of finger knuckle prints. Initially, Zernike moments are extracted for a single instance of finger knuckle print of a person and study the identification accuracy. Subsequently, the effect of identification accuracy using feature level fusion of multi-instances of knuckle prints of a person is studied. As the length of the feature vectors of different instances of knuckle print is same, one could augment the feature vectors to generate a new feature vector. The process of concatenation of feature vectors may lead to the curse of dimensionality problem. In order to handle the curse of dimensionality, the feature dimensions are reduced prior and after the feature sets fusion using Principal Component Analysis (PCA). Experiments are conducted on PolyU finger knuckle print database to assess the actual advantage of the fusion of multi-instance knuckle prints performed at the feature extraction level, in comparison to the single instance knuckle print. Further, extensive experimentations are conducted to evaluate the performance of the proposed method against subspace methods.