Real and Altered Fingerprint Classification Based on Various Features and Classifiers

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 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.

Left or Right Hand Classification from Fingerprint Images Using a Deep Neural Network

Fingerprint security technology has attracted a great deal of attention in recent years because of its unique biometric information that does not change over an individual’s lifetime and is a highly reliable and secure way to identify a certain individuals.AFIS(Automated Fingerprint Identification System)is a system used by Korean police for identifying a specific person by fingerprint.The AFIS system,however,only selects a list of possible candidates through fingerprints,the exact individual must be found by fingerprint experts.In this paper,we designed a deep learning system using deep convolution network to categorize fingerprints as coming from either the left or right hand.In this paper,we applied the Classic CNN(Convolutional Neural Network),AlexNet,Resnet50(Residual Network),VGG-16,and YOLO(You Only Look Once)networks to this problem,these are deep learning architectures that have been widely used in image analysis research.We used total 9,080 fingerprint images for training and 1,000 fingerprint to test the performance of the proposed model.As a result of our tests,we found the ResNet50 network performed the best at determining if an input fingerprint image came from the left or right hand with an accuracy of 96.80%.

Fingerprint Directional Image Partitioning Based on Genetic Algorithm

In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.