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Real and Altered Fingerprint Classification Based on Various Features and Classifiers

Saif Saad Hameed, Ismail Taha Ahmed*, Omar Munthir Al Okashi

College of Computer Sciences and Information Technology, University of Anbar, Anbar, Iraq

* Corresponding Author: Ismail Taha Ahmed. Email: email

Computers, Materials & Continua 2023, 74(1), 327-340.


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 reliable method to identify people. The recognition of fingerprints has become a standard procedure in forensics, and different techniques are available for this purpose. Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models. Therefore, 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 ones. In order to improve fingerprint classification accuracy, our proposed method used the most effective texture features and classifiers. Discriminant 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 inputs. The performance of the classifiers is determined by assessing a range of feature sets, and the most accurate results are obtained. The 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 times. Three distinct degrees of obliteration, central rotation, and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints. The proposal achieved massive success with a classification accuracy reaching 99%. The experimental results indicate that the proposed method for fingerprint classification is feasible and effective. The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.


Cite This Article

APA Style
Hameed, S.S., Ahmed, I.T., Okashi, O.M.A. (2023). Real and altered fingerprint classification based on various features and classifiers. Computers, Materials & Continua, 74(1), 327-340.
Vancouver Style
Hameed SS, Ahmed IT, Okashi OMA. Real and altered fingerprint classification based on various features and classifiers. Comput Mater Contin. 2023;74(1):327-340
IEEE Style
S.S. Hameed, I.T. Ahmed, and O.M.A. Okashi "Real and Altered Fingerprint Classification Based on Various Features and Classifiers," Comput. Mater. Contin., vol. 74, no. 1, pp. 327-340. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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