Real and Altered Fingerprint Classification Based on Various Features and燙lassifiers

Abstract

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.

Research on Local Fingerprint Image Differential Privacy Protection Method Based on Clustering Algorithm and Regression Algorithm Segmentation Image

“…The allocation of the privacy budget is dynamically determined based on the number of pixels in the local region, and a method for allocating the privacy budget according to the pixel count in the local region is considered. By calculating the remaining number of pixels to be protected, denoted as (38), and determining the remaining privacy budget allocation, denoted as 1 left j  − in (39), we can illustrate the dynamic distribution process of privacy budget using (40) to (44).…”

“…The characteristics of biometric fingerprinting should encompass universality, ensuring that every individual possesses this biometric trait, uniqueness, guaranteeing sufficient differences in biometric traits among individuals, persistence, indicating the stability of the biometric trait over time, and collectability, enabling quantitative measurement of the biometric trait. Fingerprint features can be classified into three levels ranging from coarse to fine-grained details [40]- [41]. The first level comprises the ridge direction field and frequency map, while the second level consists of map.…”