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Real and altered Fingerprint Classification Based on Various Features and Classifiers

Computers, Materials & Continua 2023年第1期74卷 327-340页

作者： Saif Saad Hameed Ismail Taha Ahmed omar munthir al okashi College of Computer Sciences and Information Technology University of AnbarAnbarIraq

Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify *** recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this *** current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification ***,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake *** order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and *** Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as *** performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are *** proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten *** distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine *** proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and *** experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.

关键词： Fingerprint classification HOG SFTA discriminant analysis(DCA)classifier gaussian discriminant analysis(GDA)classifier SOCOFing

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