Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal authentication using hand images
Pattern Recognition Letters
Personal recognition based on an image of the palmar surface of the hand
Pattern Recognition
Palmprint verification based on principal lines
Pattern Recognition
Palmprint image enhancement using phase congruency
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
Image and Vision Computing
Personal authentication using multiple palmprint representation
Pattern Recognition
Texture-based palmprint retrieval using a layered search scheme for personal identification
IEEE Transactions on Multimedia
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Authentication time is the main and important part of the authentication system. Normally the response time should be fast but as the number of persons in the database increases, there is probability of more response time taken for authentication. The need of fast authentication system arises so that authentication time matching time is very less. This paper proposes a sliding window approach to make fast authentication system. The highlight of sliding window method is constant matching time, fast and can match translated images also. Several palmprint matching methods like match by correlation etc. are dependent upon the number of corners detected and so is the matching time. In sliding window method, matching time is constant as the numbers of matching operations are limited and the matching time is independent of the number of corners detected. The palmprint corner features extracted using two approaches Phase Congruency Corner Detector and Harris Corner Detector are binarized so that only useful information features is matched. The two approaches of Phase Congruency Corner Detector and Harris Corner Detector, when matched with hamming distance using sliding window can achieve recognition rate of 97.7% and 97.5% respectively.