Abstract
Finger knuckle-print biometric system has widely us ed in modern e-world. The region of interest is needed as the key for the feature extraction in a g ood biometric system. The symmetric discrete orthonormal stockwell transform provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture feature s in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods. Thi s motivates us to propose a new local and global feature extractor. For the finger knuckle-print, th e local and global features are critical for an ima ge observation and recognition. For the finger knuckle -print, the local and global information are critic al for an image observation and recognition. The local features of an enhanced finger knuckle-print image are extracted using symmetric discrete orthon ormal stockwell transform. The Fourier transform of an image is obtained by increasing the scale of symmetric discrete orthonormal stockwell transform to infinity. The Fourier transform coefficients ext racted from the finger knuckle-print image is considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The gl obal feature is exploited to refine the arrangement of finger knuckle-print images in matching. The pro posed scheme makes use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance o f two finger knuckle-print images. The investigational results indicate that the proposed work outperforms an existing works with an equal error rate of 0.0045 and 100% correct recognition r ate on the finger knuckle-print database.
Highlights
The local orientation information is extracted by Gabor filter and Fourier transform of an image is taken as global features obtained by rising the scale of the Gabor filter was conducted by (Zhang et al, 2011). (Kumar et al, 2013a) proposed the Fuzzy binary decision tree algorithm which is used for decision making on two classes: Genuine and imposter using matching scores computed from the biometric databases
Performance of the propsed system is calculated using the selected Finger Knuckle-Print (FKP) ROI images extracted from the hand images of the individual databases. 5 images per finger knuckle-print are taken for training and 2 images are used for testing
Performance of the propsed system has been analyzed with the help of four metrics like Equal Error Rate (EER), Detection Error Trade-off (DET), Correct Recognition Rate (CRR) and Receiver Operating Characteristics (ROC) curves
Summary
Passwords, PIN, are used for identity verification in biometric system is found to be safer and more computer systems Sometimes these metrics may often secure than keys or passwords. The finger knuckle-print is extremely distinctive due to creases and skin folds and it is considered as a typical biometric verification system. It is not destroyed since people typically hold stuffs with an inside of the hand. The finger knuckle-print feature has a large likely to be broadly identified as a biometric authentication system. Finger knuckle-print biometric system is classified into local based methods and global based methods proposed by (Muralidharan and Chandrasekar, 2012) based on both features
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