Classification Of Fingerprint Images Research Articles

Electronic anti-theft protection for vehicles of people with special needs

The article presents an electronic anti-theft protection subsystem that was incorporated into the vehicle control system dedicated to people with special communication needs. The aim of the work was to design and test an electronic subsystem allowing for the acquisition and quick analysis of fingerprint images, checking whether they belong to the vehicle owner and generating a signal allowing the vehicle to be started. The following hypothesis was put forward: it is possible to use artificial intelligence for accurate, automatic classification of fingerprint images acquired in a system with a single-chip microcomputer in order to identify a person. The research niche includes the use of biometrics in electronic anti-theft security adapted to people with special needs. This solution reduces the risk of theft and unauthorized use of special vehicles tailored to the owners' individual needs. The use of a commercial algorithm offered by the sensor manufacturer or the use of artificial neural networks for the classification of fingerprints was considered. The results of research on the accuracy of fingerprint recognition obtained from the developed subsystem are presented. The experiments performed based on the NASNetLarge artificial neural network model confirmed the possibility of achieving recognition accuracy for the test set of 99.99%. Additionally, practical aspects of the applicability of the presented device in an electric vehicle control system supporting the movement of people with physical and/or intellectual disabilities were discussed. The presented solution, after minor modifications, can be used in access security systems for protected rooms, as well as vehicles and military equipment.

An Investigation of Benford’s Law Divergence and Machine Learning Techniques for Intra-Class Separability of Fingerprint Images

Protecting a biometric fingerprint database against attackers is very vital in order to protect against false acceptance rate or false rejection rate. A key property in distinguishing biometric fingerprint images is by exploiting the characteristics of these different types of fingerprint images. The aim of this paper is to perform an intra-class classification of fingerprint images using Benford's law divergence values and machine learning techniques. The usage of these Benford’s law divergence values as features fed into the machine learning techniques has proved to be very effective and efficient in the intra-class classification of biometric fingerprint images. The effectiveness of our proposed methodology was demonstrated on five datasets resulting in a total of 367 samples. All the machine learning techniques used in this experiment were trained using the k-fold cross validation and the dataset was split into ten times (10-folds). The models achieved high intra-class classification mean accuracies of 99.72% for the Convolutional Neural Networks (CNN), and 95.90% for the Naïve Bayes. Again, the Decision Tree and Logistic Regression, achieved accuracies of 95.62%, and 94.47%, respectively. These results showed that Benford’s law features and machine learning techniques, especially the CNN and Naïve Bayes can be effectively applied for the intra-class classification of fingerprint images. The implication of these results is that the different types of fingerprint images can be effectively discriminated using Benford's law divergence values and machine learning technique for forensics and biometrics applications.

АЛГОРИТМЫ КЛАССИФИКАЦИИ ОТПЕЧАТКОВ ПАЛЬЦА

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

АЛГОРИТМЫ КЛАССИФИКАЦИИ ОТПЕЧАТКОВ ПАЛЬЦА

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

Gamma Distribution of FAST Feature for Statistical Image Modelling in Fingerprint Image Classification

A unique method for modelling Features from accelerated segment test (FAST) with the Gamma distribution for statistical image data is introduced. Instead of using an image's FAST feature instantly; we design the FAST function to decrease the other global extraction function. The method of moment is used to predict Gamma distribution parameters. FAST's mathematical depiction is the depiction of matrix and is too complicated to be implemented in the ranking of images. We are therefore proposing a fresh statistical function to display the picture in a few dimensional numbers. Our proposed feature utilizes FAST method of Gamma distribution that can be used directly in the identification of fingerprint images. We demonstrate that the Gamma distribution works with FAST and has been effectively implemented in the identification of fingerprint images. To show the benefits of the proposed feature, Shivang Patel Fingerprint Database is used on which classifier evaluation and state-of - the-art comparison are performed.

Fingerprint image classification using local diagonal and directional extrema patterns

Proper classification of fingerprints still poses difficult issues in large-scale databases due to ambiguity in intraclass and interclass structures, discontinuity in low-quality images, and ridges. To address these challenges, we propose a feature named local diagonal and directional extrema pattern (LDDEP) as a descriptor for classification of fingerprints. The proposed method utilizes first-order derivatives to find values and indices of local diagonal and directional extremas. The local extrema values are then compared with the central pixel intensity value to find the correlation with the neighbors. Eventually, the descriptor is generated with the help of the indices and local extrema values. Furthermore, the proposed descriptor is fed into K-nearest neighbor and support vector machine (SVM) for classifying the fingerprint images into four and five groups, respectively. The LDDEP descriptor is compared with the existing methods on two databases, namely National Institute of Standards Technology Special Database 4 (NIST SD 4) and Fingerprint Verification Competition (FVC). Our experiments have shown that, on the 4000 image NIST SD 4 test dataset, the proposed descriptor achieved a classification accuracy of 95.15% for five classes and 96.85% for four classes for half of the dataset, and an accuracy of 95.5% for five classes and 96.63% for four classes for the entire test dataset using SVM classifier. Similarly, FVC databases for the LDDEP descriptor gave classification accuracy of 98.2% using SVM classifier. The proposed method gave higher accuracies compared to the existing methods.

Local adjacent extrema pattern for fingerprint image classification

Fingerprint classification is as yet difficult issue due to incomplete information in ridges and low quality images. To address these problems, we propose a new and robust Local Adjacent Extrema Pattern (LAEP) is a feature descriptor for fingerprint classification. The proposed descriptor finds it values based on indexes of local adjacent extremas using first order derivatives. The intensity values of the local extremas are compared with center pixel intensity value to employ the correlation of central pixel with its neighbors. Finally, the descriptor is generated on the support of the indexes and local extremas values. Support Vector Machine (SVM) is utilized for classification of fingerprint images into five classes. To prove the effectiveness of the proposed descriptor, we have tested on Indraprastha Institute of Information Technology (IIIT) - Indian rural fingerprint database for classification. In addition, the classification results of the proposed descriptor are compared with the existing methods. The resultant LAEP descriptor proved better classification accuracy than the previous methods.

Classification of fingerprint images with the aid of morphological operation and AGNN classifier

The uniqueness, public recognition, firmness, and their least jeopardy of fingerprints made an extensively and proficiently utilized personal authentication metrics. Fingerprint technology is a biometric method that is used to recognize persons on the basis of their physical traits. These physical forms comprise of ridges and valleys prevailing on the surface of fingertips. Fingerprint images are direction-oriented pattern fashioned using ridges and valleys. The reputation of the fingerprint image regulates the durability of a fingerprint authentication scheme. For enhancing the restrictions of prevailing fingerprint image augmentation approaches we have proposed an effectual method to pact with various fingerprint images. The proposed methodology alienated into three modules. Primarily, the fingerprint image is endangered to denoising procedure where Wave atom transform is used. Once this procedure is accomplished the image augmentation is achieved for improving the classification rate. The morphological operation is used in our proposed technique in order to augment the image. The morphological operators such as dilation and area opening are used here for improvement. Finally the ordering of fingerprint image is done. Adaptive Genetic Neural Network (AGNN) is used for classification of images efficiently.

Metaheuristic Optimization Algorithms Based Feature Selection for Fingerprint Image Classification

Fingerprint biometric plays a vital role to authenticate a person in a right way. Fingerprint classification is crucial which reduces the search time of a large database while authentication. In this research work, a novel fingerprint image classification system is proposed with the exploit of metaheuristic optimization based feature selection methods. It involves four main tasks: pre-processing, feature extraction, feature selection, and classification. Initially, the fingerprint is denoised using undecimated wavelet transform. Then, a set of rich discriminate Local Binary Pattern (LBP) features are extracted from the enhanced image. Metaheuristic models such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are studied for feature selection. Finally, the fingerprint images are classified using Back Propagation Neural Network (BPN). In this research, experiments have been conducted on real-time fingerprint images collected from 150 subjects each with ten orientations using eSSL ZK7500 fingerprint sensor and also on the NIST-4 dataset. The experimental results are compared in terms of Precision, Recall, F-measure, Accuracy, and Error rate with other benchmark classification techniques such as Support Vector Machine (SVM) and Multi Layer Perceptron (MLP), to conclude the efficacy of the proposed approach.

A Review on Classification of Fingerprint Images

Fingerprint is most widely used in individual identification especially in civilian applications, forensic laboratories etc. due to large bio-invariant characteristics of human fingerprints. A search over all the records in a data base takes a very long time and deteriorates accuracy as database is very large. To avoid this inconvenience, automatic fingerprint classification method is employed that does not require any user's interaction, reduce the search space and time and also increases accuracy of identifications. Classifying fingerprint images is a very difficult pattern recognition problem, due to the small interclass variability, the large intraclass variability, and the presence of noise and the ambiguous properties of fingerprints. In this paper we discuss about fingerprint classification and also review existing methods that have been applied for the classification problem.

Combining Gabor filter and FFT for fingerprint enhancement based on a regional adaption method and automatic segmentation

Fingerprints are the best biometric identity mark due to the consistency during life time and uniqueness. To increase the classification accuracy of fingerprint images, it is necessary to improve image quality which is a key role for correct recognition. In other words, enhancing the fingerprint images leads us to obtain better results in classification of fingerprint images. Although Gabor filter and fast Fourier transform (FFT) are used to enhance fingerprint images, Gabor filter acts better than FFT in detection of incorrect ridge endings and ridge bifurcation, while FFT tries to connect broken ridges together and fill the created holes. This paper tries to enhance gray-scale fingerprint images by combining the Gabor filter and FFT in order to get benefit from the advantages of each enhancing filter (Gabor filter and FFT). A method is proposed for fingerprint image segmentation based on the image histogram and density. By employing the proposed method which enhances the fingerprint images using the better enhancing filter in each part, the experimental results show that the whole finger print is better enhanced, and consequently, it leads to a better recognition rate.

Classification of fingerprint images to real vs. spoof

Biometric identification is becoming a leading technology for identity management and security systems. Nonetheless, the use of counterfeit elastic fingerprints (|spoofing|) may break these measures. In this paper, we address the problem of fingerprint spoofing based solely on image features extracted from 2D fingerprint images. By combining several low-accuracy methods, a robust high-performance classifier for real vs. fake fingerprint images is constructed. Its high accuracy is demonstrated on a large fingerprint database. The method thus shows high potential for improving existing fingerprint authentication devices.

Geometric framework for fingerprint image classification

Given a digitized fingerprint image, we would like to classify it into one of several types already established in the literature. In this paper, we consider five types for classification: double loop, whorl, left loop, right loop, and arch. We illustrate the use of a geometric framework for a systematic top-down classification of the foregoing types. From the double-loop type down to the arch type in the order given above, the framework employs both a geometric grouping and a global geometric shape analysis of fingerprint ridges to accomplish the required task. These processes are based on the framework's underlying B-spline representation and interpretation of the ridges.

Fingerprint image analysis for automatic identification

Most of the papers on fingerprints deal with classification of fingerprint images. Fingerprint databases being large (in the range of millions), the effort in matching of fingerprints within a class or when the class is unknown, is very significant. This requires fingerprint image analysis and extraction of the “minutiae” features, which are used for matching FPs. In this paper a scheme of preprocessing and feature extraction of fingerprint images for automatic identification is presented, which works even if the pattern class is unknown. The identification of fingerprints is based on matching the minutiae features of a given finger-print against those stored in the database. The core and delta information is used for classification and for registration while matching. These algorithms have been tested for more than 10,000 fingerprint images of different qualities. The results are manually verified and found to be very good for practical application. A few sample results are presented.

Classification of fingerprint images using a neural network

AbstractFingerprints are an effective means of identifying individuals, and several fingerprint comparison systems have already been developed. However, since it becomes difficult to recognize fingerprints with ease as the number and diversity of fingerprint patterns increases, it becomes desirable to create an efficient database for fingerprint matching with the goal of developing an automatic fingerprint classification system. Here an automatic classification system is imposed for fingerprint images in which the discrimination system is a multilayer neural network composed of five subnetworks to carry out macrofeature extraction on the fingerprint image. A total of 50 training examples, 10 per category, and 500 testing samples were presented to the network. According to the obtained results, the network achieved an 86 percent classification rate for the first candidate and a 99 percent classification rate for the first two candidates. The state of the network was analyzed after training, and the reasons for the system effectiveness and its classification error were studied.