Fingerprint Recognition System Research Articles

A New Algorithm for Locating and Extracting Minutiae from Fingerprint Images

Fingerprints are considered as the oldest and most widely used in the world for biometric identification. Every person has unique and permanent fingerprints. Most automatic fingerprint recognition systems are based on features formed from lines known as minutiae. Building a database of unique minutiae is very important in the security systems because it concerns the identification of the person committing a crime through the latent left in the crime scene. This research presents a new algorithm to give a minutia a unique value leading to accelerating the search process for a person. The algorithm splits the fingerprint image into four sections, then calculates the values for each minutia in each section and stores it in a database that is designed for this purpose. This research provides a great opportunity and additional options to fingerprint experts in order to solve many cases that are still undiscovered while searching for the latent in the database of minutiae. The results of testing this algorithm were very successful, very encouraging and helpful to fingerprint experts in their work.

A BROAD SURVEY ON FEATURE EXTRACTION METHODS FOR FINGERPRINT IMAGE ANALYSIS

This paper focused on the study of various feature extraction techniques applied for fingerprint identification, verification and classification as it is the most important step for image processing. Feature extraction techniques are classified into local (low level) and global (high level) features. Global features such as arch, loop, delta and whorl where as local features such as ridge end and bifurcation called minutiae are majors of automatic fingerprint recognition system. In this study, it has been observed that most of the fingerprint recognition systems are based on minutiae features. In this paper we analyze the various feature extraction methods used so far with their mathematical background to the readers.

Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model

Biometric devices play an integral role in consumer’s daily life, providing a seamless environment. However, it is essential to measure the usability of biometrics, owing to the elements of biometrics satisfying both usability and security. This study redefines the elements of biometrics pertaining to usability determined in previous studies and adds elements of psychological relevance, such as privacy concerns. To organize the interrelated usability structure systemically, this paper applies the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) to derive the usability structure. Thereupon, the established structure is applied in the clustered weighted Analytical Network Processes (ANP) to generate the proposed usability evaluation model. By these methods, the pertinent relationships between the factors are clarified and the weight of each element is determined. In the empirical study, 106 students measured usability of the fingerprint recognition system, iris recognition system, and facial recognition system employing our usability evaluation model. The results of this model generate the quantitative score of usability for biometric systems and suggest strategies to increase the score. The proposed usability evaluation model can comprehensively assist usability practitioners to evaluate biometric systems.

Biometric Based Attendance Management System using Cloud

Biometrics is the technical term for body measurements and calculations. Biometric technology refers to identification and verification of individuals by analyzing the human body characteristics. In modern times, there has been an increase in the number of identity theft experienced on a day to day basis. Companies, Organizations and Institutions are generally concerned over the levels of employee absence in the workplace, where security is essential, access to restricted areas are controlled and monitored through a system. Most institutions find it hard to managing student’s attendance during lecture periods. Manually taking attendance by hand and preserving it over a long period adds to the difficulty and wastes a lot of time. For these reasons an effective system is proposed in this paper to solve the problem of manual attendance. Here a system would take attendance electronically with the help of a fingerprint recognition system and all records are saved in cloud or locally in the device. A staff biometric attendance would contain a system to calculate attendance of the staff and do further calculations such as monthly attendance summary and calculate wages based on attendance, etc. In addition, this system if implemented properly could increase productivity, can reduce truancy, lateness in organization and institution.

Hybrid minutiae and edge corners feature points for increased fingerprint recognition performance

In general, most fingerprint recognition systems are based on the minutiae feature points. When matching two fingerprint images, the goal in most recognition systems is to find the optimal transformation model that aligns their feature points in order to find among them the number of matched or aligned points and then generate a matching score. A major problem in feature extraction stage is that when the fingerprint image is of a poor quality due to skin conditions and sensor noise, that leads to many broken ridges in the image caused by cutline. In this case, the extraction of minutiae leads to a lot of spurious points and the performance of the system will degrade. Usually, image enhancement techniques are applied as preprocessing step to overcome this problem. In this work, we propose to use corner points on fingerprint ridges as new features in addition to the ridges minutiae in order to improve the recognition performance. Every ridge is decomposed into several straight edges (SEs). A straight edge is defined as a straight link of ridge points. On a ridge, the head of the first straight edge and the tail of the last one are two minutia and the intersections of the SEs are the ridge corners. Thus, we propose to use a ridge as primitive rather than individual points for matching. This primitive is a structure consisting of groups of both feature points which are minutiae and corners belonging to the same ridge. Based on this primitive, an intelligent matching technique is introduced using sets of feature points on the same primitive. As a result, the recognition performance is increased since it is based on ridge primitive matching rather than individual minutiae matching. Finally, our experimental results compared with those obtained by other well-known techniques in the literature demonstrate the effectiveness and efficiency of our proposed algorithm.

Automotive Start–Stop Engine Based on Fingerprint Recognition System

Automated vehicle security system plays an important rule in nowadays advance automotive technology. One of the methods which can be applied for a security system is based on biometric identification system. Fingerprint recognition is one of the biometric systems that can be applied to the security system. In this work, fingerprint recognition system to start the motorcycle engine is developed. The fingerprint of the owner and other authorized persons will be stored into the database, then while the time of starting the engine of the vehicle, the fingerprint will be validated with the database. The minutiae extraction method is applied to find the difference of fingerprint each other after turn the image into grayscale and thinning. After the extraction, the next step is finding the ridge edge and bifurcation. The result of the image will be used as input to the Artificial Neural Network (ANN) to recognize authorized person only. The experiment of fingerprint recognition system shows that automatic start-stop engine using fingerprint recognition system based minutiae extraction and Artificial Neural Network (ANN) has accuracy 100 % and 100 %, respectively.

Performances Enhancement of Fingerprint Recognition System Using Classifiers

Fingerprint recognition is best known and generally used as a biometric technology because of their high acceptability, immutability, and uniqueness. A fingerprint consists of ridges and valleys pattern also known as furrows. These patterns fully develop in the mother’s womb and remain constant throughout the whole lifetime of the individual. The ridge bifurcation and ridge termination are the main minutiae features that are extracted for identification of individuals in fingerprint recognition system. The aim of this paper is to enhance the performance of the fingerprint recognition systems using classifiers. To achieve the aim, fingerprints from the FV2002 database are used, before these fingerprints are evaluated, image enhancement and binarization is applied as a pre-processing on fingerprints, by combining many methods to build a database of fingerprint features having minutia marking and minutia feature extraction. The fingerprint recognition is presented by image classification using MATLAB classifiers, i.e., Decision Tree, Linear Discriminant Analysis, medium Gaussian support vector machine (MG-SVM), fine K-nearest neighbor, and bagged tree ensemble. The aim of this paper is to make a comparison between classifiers for performance enhancement of the fingerprint recognition system. The MG-SVM classifiers significantly give the highest verification rate of 98.90% among all classifies used.

Unrolling Post-Mortem 3D Fingerprints Using Mosaicking Pressure Simulation Technique

Post-mortem fingerprints are a valuable biometric used to aid in the identification of a deceased individual. However, fingerprints from the deceased undergo decomposition leading to indefinite structure when compared to ante-mortem fingerprints. Moreover, the performance of the existing two-dimensional (2D) fingerprint recognition systems is still below the expected potential. These problems arise because fingerprints are generally captured by manipulating a finger against a plane. In post-mortem fingerprint recovery, the decedent's finger must go through several reconditioning processes to prevent the rapid onslaught of decomposition. To address these deficiencies associated with the 2D systems, three-dimensional (3D) scanning systems have been employed to capture fingerprints. The 3D technology is still in its transient phase and is limited primarily by 1) the lack of existing 3D databases; 2) the deficiency of 3D-to-2D fingerprint image mapping algorithms, 3) the incapacity to model and recreate the 2D fingerprint capturing procedure to improve 3D-2D fingerprint verification; and 4) the inability to apply traditional fingerprint unrolling techniques on post-mortem 3D fingerprints. This paper presents a novel method to perform post-mortem 3D fingerprint unrolling and pressure simulation to produce fingerprint images that are compatible with 2D fingerprint recognition systems. The thrust of this paper strives to: 1) develop a correspondence between 3D touchless and contact-based 2D fingerprint images; 2) model fingerprints with deformities to provide a viable fingerprint image for matching and; 3) develop a mosaic pressure simulation (MPS) algorithm to recreate the effects of 2D fingerprint capturing procedure.

A review on biometric Technology in Healthcare

Biometrics system can be defined as the body's measurement used for identification and authentication process. Biometrics is used to detect and examine the measurable characteristics of the individuals. Biometric technology used in various applications in different fields. In emerging medical field, biometric technology like fingerprint recognition systems has been developed for accessing the patient data or medical records in a secured way.

Face Recognition as a Biometric Application

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from the given image with faces within a database. It is also described as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics. It is typically used as access control in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Although the accuracy of facial recognition system as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process. Recently, it has also become popular as a commercial identification and marketing tool. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images and video database, among others. The use of facial recognition has recently become a very debatable subject and has been criticized more and more because it was considered an unethical tool used to spy on the public. The reason for such criticism is, however, largely due to the lack of information and regulation of this technology. Used proportionally and responsibly, facial recognition can and should be beneficial. It has the capacity to do much more to increase security in the future-from street crime to airport security. Armed war crime has dominated UK titles throughout the year. Recent statistics indicate that the number of people who benefited from the emergency assistance due to armed attacks increased by almost 40% compared to the two years ago, while the number of children under 18 years of age with stab wounds is increasing by 86 % in only four years. Face recognition has become a normal activity in many airports around the world. Many people today have a so-called biometric passport that allows them to go faster to the gate without having to be controlled. The facial recognition used in this way has significantly reduced waiting times for passport control but also has the ability to increase security in and around airports. Face recognition thus allows officers to identify an individual more quickly and accurately than the human eye. While some critics may worry about technology-related confidentiality issues, airports have shown that the use of facial recognition has improved security as well as speeding up processes such as check-in and in the future, even procedures boarding. When used correctly and proportionally, facial recognition can help protect the public and improve national security on multiple fronts. Similarly, advanced technology can recognize a person seen on CCTV security systems at the crime scene, justifying a person's stop and search. The ability to check in real time whether a person is on the list of criminal investigations adds an added advantage to the decision-making process before stopping and searching, thus lowering the probability of discrimination.

Face Recognition as a Biometric Application

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from the given image with faces within a database. It is also described as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics. It is typically used as access control in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Although the accuracy of facial recognition system as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process. Recently, it has also become popular as a commercial identification and marketing tool. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images and video database, among others. The use of facial recognition has recently become a very debatable subject and has been criticized more and more because it was considered an unethical tool used to spy on the public. The reason for such criticism is, however, largely due to the lack of information and regulation of this technology. Used proportionally and responsibly, facial recognition can and should be beneficial. It has the capacity to do much more to increase security in the future-from street crime to airport security. Armed war crime has dominated UK titles throughout the year. Recent statistics indicate that the number of people who benefited from the emergency assistance due to armed attacks increased by almost 40% compared to the two years ago, while the number of children under 18 years of age with stab wounds is increasing by 86 % in only four years. Face recognition has become a normal activity in many airports around the world. Many people today have a so-called biometric passport that allows them to go faster to the gate without having to be controlled. The facial recognition used in this way has significantly reduced waiting times for passport control but also has the ability to increase security in and around airports. Face recognition thus allows officers to identify an individual more quickly and accurately than the human eye. While some critics may worry about technology-related confidentiality issues, airports have shown that the use of facial recognition has improved security as well as speeding up processes such as check-in and in the future, even procedures boarding. When used correctly and proportionally, facial recognition can help protect the public and improve national security on multiple fronts. Similarly, advanced technology can recognize a person seen on CCTV security systems at the crime scene, justifying a person's stop and search. The ability to check in real time whether a person is on the list of criminal investigations adds an added advantage to the decision-making process before stopping and searching, thus lowering the probability of discrimination.

A Robust Fingerprint Recognition Technique Applying Minutiae Extractors and Neural Network

Fingerprint recognition is one of the interesting and mature biometric technique for personal identification or authentication application. The objective of the work is to retrieve personal data by using the fingerprint. In this paper, the fingerprint of a person is captured via Digital Persona U.are.U 4500 fingerprint scanner. The input fingerprint is firstly enhanced using contrast stretching and two morphological operations (dilation and erosion). Next, 160x160 size of the center region of the fingerprint is cropped and extracted minutiae such as ridges and bifurcations. These minutiae are used as the features of a particular fingerprint. These features are put into the Neural Network for person recognition. Various experiments will confirm and prove the accuracy and performance of the proposed fingerprint recognition system

Novel Adaptive Auto-Correction Technique for Enhanced Fingerprint Recognition

Fingerprints are the most used biometric trait in applications where high level of security is required. Fingerprint image may vary due to various environmental conditions like temperature, humidity, weather etc. Hence, it is necessary to design a fingerprint recognition system that is robust against temperature variations. Existing techniques such as automated and non-automated techniques are not real time analysis (adaptive). In this paper, we propose an adaptive auto correction technique called Reference Auto-correction Algorithm. This proposed algorithm corrects user reference fingerprint template automatically based on captured fingerprint template and the matching score obtained on daily basis to improve the recognition rate. Analysis is carried out on 250 fingerprint templates stored in the database of 10-users captured at varying temperature from 25°C to 0°C. The experimental result shows 40% improvement in the recognition rate after applying auto correction algorithm.

Slim-ResCNN: A Deep Residual Convolutional Neural Network for Fingerprint Liveness Detection

Fingerprint liveness detection has gradually been regarded as a primary countermeasure for protecting the fingerprint recognition systems from spoof presentation attacks. The convolutional neural networks (CNNs) have shown impressive performance and great potential in advancing the state-of-the-art of fingerprint liveness detection. However, most existing CNNs-based fingerprint liveness methods have a few shortcomings: 1) the CNN structure used on natural images does not achieve good performance on fingerprint liveness detection, which neglects the inevitable differences between natural images and fingerprint images; or 2) a relative shallow architecture (typically several layers) has not paid attention to the capability of deep network for spoof fingerprint detection. Motivated by the compelling classification accuracy and desirable convergence behaviors of the deep residual network, this paper proposes a new CNN-based fingerprint liveness detection framework to discriminate between live fingerprints and fake ones. The proposed framework is a lightweight yet powerful network structure, called Slim-ResCNN, which consists of the stack of series of improved residual blocks. The improved residual blocks are specifically designed for fingerprint liveness detection without overfitting and less processing time. The proposed approach significantly improves the performance of fingerprint liveness detection on LivDet2013 and LivDet2015 datasets. Additionally, the Slim-ResCNN wins the first prize in the Fingerprint Liveness Detection Competition 2017, with an overall accuracy of 95.25%.

Fingerprint growth model for mitigating the ageing effect on children’s fingerprints matching

Nowadays, the majority of fingerprint quality, matching and feature extraction algorithms are developed and trained on fingerprints of adults. Accordingly, the processing of children’s fingerprints presents performance issues derived for the most part from: (1) their smaller size and finer ridge structure; (2) their higher variability over time due to the displacement of minutiae induced by growth. The present article is focused on the second factor. The rapid growth of children fingerprints causes a significant displacement of the minutiae points between samples of the same finger acquired with a few years distance from each other. This displacement results in a decrease of the accuracy of fingerprint recognition systems when the reference and probe sample drift apart in time. This effect is known as biometric ageing. In the present study we propose to address this issue by developing and validating a minutiae-based growth model, derived from a database of over 60,000 children’s fingerprints, acquired in real operational conditions, ranging between 5 and 16 years of age, with a time difference between fingerprint pairs re-enrolments of up to 6 years. We analyze two potential application scenarios for the developed growth model. On one hand, we use the model to grow children’s fingerprints in order to spread out the minutiae points to attain sizes similar to those of a sample captured at a later point in time. On the other hand, we apply the model to rejuvenate fingerprints enrolled at a later stage by contracting the minutiae points so that their location is more similar to those of a sample acquired earlier. In both scenarios, the application of the growth model to produce artificially grown/rejuvenated fingerprint minutiae templates results in a significant improvement of the matching scores compared to the ones produced by original fingerprints.

Fingerprint Identification Using FPGA with a Newly Designed Architecture

The Aadhaar project was recently introduced by the Unique Identification Authority of India (UIDAI) to provide a Unique Identification (UID) number to every resident of India. Biometric and demographic information are used to create a unique identification number (Aadhaar). You may use your Aadhaar number to get a subsidy on LPG cylinders, a driving licence, a PAN card and a bank account. The software industry is crucial to the success of the Aadhaar system. The suggested work, including the pre-processing stage, fingerprint recognition process, and fingerprint recognition system, is carried out across three platforms, including MATLAB, field programmable gate array (FPGA), and application specific integrated circuit (ASIC). In order to improve the processing speed, resource utilisation, power consumption, and recognition rate of the proposed job, the FPGA is programmed with two specialised hardware methods: the CORDIC algorithm and IP cores. The suggested work largely focuses on fixing the processing time and accuracy issues. The concept is converted into an ASIC using FPGA technology with the goal of reducing power consumption and resource utilisation without sacrificing performance. In contrast to FPGA implementation, which makes use of look-up tables (LUTs) and customizable logic blocks (CLBs), ASIC implementation makes use of the basic cells directly. When compared to FPGA design, ASIC development speeds up recognition by 20%.

Analysis of Different M-Band Wavelet Filters for Face Recognition using Nearest Neighbor Classifier

<span>Face recognition system is one of the most interesting studied topics in computer vision for past two decades. Among the other popular biometrics such as the retina, fingerprint, and iris recognition systems, the face recognition is capable of recognizing the uncooperative samples in a non-intrusive manner. Also, it can be applied to many applications of surveillance security, forensics, border control, digital entertainment where face recognition is used in most. In the proposed system an automatic face recognition system is discussed. The proposed recognition system is based on the Dual-Tree M-Band Wavelet Transform (DTMBWT) transform algorithm and features obtained by varying the different filter in the DTMBWT transform. Then the different filter features are classified by means of the K-Nearest Neighbor (KNN) classifier for recognizing the face correctly. The implementation of the system is done by using the ORL face image database, and the performance metrics are calculated.</span>

Live fingerprint detection using magnitude of perceived spatial stimuli and local phase information

Fingerprint recognition systems are widely used for authentication purposes in security systems. However, fingerprint recognition systems can easily be spoofed by imitations of fingerprints using various spoof materials. A compact and discriminative set of features is needed to discriminate between live and spoof fingerprints. We explore combined Shepard magnitude and orientation for live fingerprint detection using independent quantization of global and local features extracted in spatial and frequency domain. The spatial domain features that are extracted comprise of the magnitude of perceived spatial stimuli that is computed from the net variation of perceived edge information. Rotation invariance is achieved by extracting local features based on phase information of significant frequency components in the frequency domain. The concatenated feature vector associated with a fingerprint image is represented as a two-dimensional histogram. The support vector machine classifier is used to classify the fingerprint as either live or spoof. Experiments are performed on three databases, i.e., the fingerprint liveness detection (LivDet) competition databases of 2011, 2013, and 2015. Results showed a reduction in average error rate to 5.8, 2.2, and 5.3 on LivDet 2011, 2013, and 2015, respectively.

Patch-based Segmentation of Latent Fingerprint Images Using Convolutional Neural Network

ABSTRACTLatent fingerprint segmentation involves marking out all the foreground regions accurately in a latent fingerprint image, but due to poor quality images and complex background, segmentation of latent fingerprint images is one of the most difficult tasks in automatic latent fingerprint recognition systems. In this article, we propose a patch-based technique for segmentation of latent fingerprint images, which uses Convolutional Neural Network (CNN) to classify patches. CNN has recently shown impressive performance in the field of pattern recognition, classification, and object detection, which inspired us to use CNN for this complex task. We trained the CNN model using SGD to classify image patches into fingerprint and non-fingerprint classes followed by proposed false patch removal technique, which uses “majority of neighbors” to remove the isolated and miss-classified patches. Finally, based on the final class of patches, an ROI is constructed to mark out the foreground from the background of latent fingerprint images. We tested our model on IIIT-D latent fingerprint database and the experimental results show improvements in the overall accuracy compared to existing methods.

Low‐quality fingerprint classification using deep neural network

Fingerprint recognition systems mainly use minutiae points information. As shown in many previous research works, fingerprint images do not always have good quality to be used by automatic fingerprint recognition systems. To tackle this challenge, in this work, the authors are focusing on very low-quality fingerprint images, which contain several well-known distortions such as dryness, wetness, physical damage, presence of dots, and blurriness. They develop an efficient, with high accuracy, deep neural network algorithm, which recognises such low-quality fingerprints. The experimental results have been obtained from the real low-quality fingerprint database, and the achieved results show the high performance and robustness of the introduced deep network technique. The VGG16-based deep network achieves the highest performance of 93% for dry and the lowest performance of 84% for blurred fingerprint classes.