Real and Altered Fingerprint Classification Based on Various Features and Classifiers

作     者：Saif Saad Hameed Ismail Taha Ahmed Omar Munthir Al Okashi 

作者机构：College of Computer Sciences and Information TechnologyUniversity of AnbarAnbarIraq 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第1期

页      面：327-340页

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Fingerprint classification HOG SFTA discriminant analysis(DCA)classifier gaussian discriminant analysis(GDA)classifier SOCOFing 

摘      要：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.