Finger Knuckle-Print Identification Based on Local and Global Feature Extraction Using Sdost

Abstract

Finger knuckle-print biometric system has widely used in modern e-world. The region of interest is needed as the key for the feature extraction in a good biometric system. The symmetric discrete orthonormal stockwell transform provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods. This motivates us to propose a new local and global feature extractor. For the finger knuckle-print, the local and global features are critical for an image observation and recognition. For the finger knuckle-print, the local and global information are critical for an image observation and recognition. The local features of an enhanced finger knuckle-print image are extracted using symmetric discrete orthonormal 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 extracted 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 global feature is exploited to refine the arrangement of finger knuckle-print images in matching. The proposed 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 of 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 rate on the finger knuckle-print database.

Retracted: Innovative approach for multimodal fusion recognition based feature extraction using band‐limited phase‐only correlation and discrete orthonormal Stockwell transform

“…Here the state-of-the-art approach is striving to improve this biometric recognition based on the following techniques. Kumar and Premalatha [18] proposed the local and global features of enhanced FKP which is extracted using Stockwell transform. The average weighted sum of both these features is calculated based on matching distance of this knuckle testing and training image.…”

“…Here the state‐of‐the‐art approach is striving to improve this biometric recognition based on the following techniques. (i) Basic data set requirements for both EAR and FKP combination of local features of these two biometrics using Gabor feature extraction. (ii) Global features of EAR are analysed and calculated by discrete orthonormal Stockwell transform (DOST). (iii) Similarly features of FKP are determined and anlaysed by band‐limited phase‐only correlation (BLPOC). (iv) Finally all features are involved in matching stage based on feature level fusion. Kumar and Premalatha [18] proposed the local and global features of enhanced FKP which is extracted using Stockwell transform. The average weighted sum of both these features is calculated based on matching distance of this knuckle testing and training image.…”

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

“…Kumar et al [ 38 ] proposed to work on the security of multimodal frameworks by creating the biometric key from unique finger impression and FKP biometrics with its component extraction utilizing K-implies calculation. The mysterious worth is scrambled with a biometric key utilizing the asymmetric Advanced Encryption Standard (AES) Algorithm.…”

Novel Method of FKP Feature Extraction Using Mechanical Variable

“…As the surface impression of knuckle print is exterior side, the people have no contact with material on the outside of their hands unlike fingerprints. Therefore no scope of latent FKP also no criminal investigation stigma associated with printing the surface of the knuckles, therefore FKP has a high acceptance rate [1]. Moreover FKP has surface pattern that contains fine crests and texture those are easy to measure with considerable low deformation rate as shown in Figure 1, hence researchers have set their sights on FKP trait [2].…”