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
Near- and Far- Infrared Imaging for Vein Pattern Biometrics
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
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
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
Ensemble of local and global information for finger-knuckle-print recognition
Pattern Recognition
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like Finger Knuckle Print (FKP) of a person is unique and secure. In this paper, we propose a human authentication system based on FKP image of a person. Depending on the security level required by an organization that implements the proposed system, we provide two modes of security viz. basic mode and advanced mode. The Radon Transform is applied on pre-processed FKP image and Eigen values are computed. For basic mode, we compute the correlation coefficient between the set of Eigen values stored in the database and that of input image to authenticate a person. For advanced level of security, we identify the peak points in Radon graph. The successive distances between those points are calculated and are stored in a vector. Now, the elements in distance vector stored in database and that of input image are compared. Such a match is considered to be success if the difference between two such elements is lesser than the threshold value. Now, the probability of success is computed. To authenticate a person in advanced mode, we use the correlation coefficient between Eigen values and the probability. For real time implementation, suitable GUI can be developed. The basic mode of security system is found to have FAR as 6.79% and FRR as 0.0517%. The advanced system has the FAR of about 1.55% and FRR as 1.02%.