Fingerprint Recognition System Research Articles

An Improved Quality Enhancement Fingerprint Analysis

The use of fingerprint authentication as a biometric technique for secure authentication and access control has gained significant traction in recent years. This article presents an overview of the technology used for fingerprint identification, its history, as well as the fundamental components that make up a fingerprint recognition system. We also go through the many approaches to fingerprint identification techniques and algorithms, such as the minutiae-based approach, the ridge feature-based approach, and the pattern recognition-based approach. All of these are utilized in the process of authenticating fingerprints. In addition, we emphasize the limitations of fingerprint authentication as well as the security risks that are linked with it, as well as alternative methods to overcome these constraints.

Evaluation of two biometric access control systems using the Susceptible-Infected-Recovered model

This paper evaluates the effectiveness of decisions made on two single-mode biometric systems based on facial recognition and fingerprints for access control. To achieve this, we first implemented an embedded system under Arduino to allow us to open and close doors, then we programmed two biometric recognition systems, namely facial recognition and fingerprint recognition, and finally we exploited the Susceptible-Infected-Covered model without demographics to evaluate the efficiency of these two access control systems. The variables used in the analysis were the number of individuals enrolled in the biometric system to be subject to access control (Susceptible), the number of individuals enrolled in the biometric system and denied access by the system, as well as the number of individuals not enrolled in the biometric system and allowed access by the system (Infected), and the number of false acceptance rates and false rejection rates at time t in the systems (Retrieved). In a sample of 600 individuals, of which 300 were enrolled and 300 were not, our two simple modal access control systems each obtained the following results: 270 true positives, 30 false negatives, 48 false positives and 252 true negatives for the facial recognition system, compared to 288 true positives, 12 false negatives, 24 false positives and 276 true negatives for the fingerprint recognition system, which constitute our confusion matrix. Based on this confusion matrix, we were able to exploit the false rejection rates and false acceptance rates to correct for these inconveniences using the SIR model, i.e. 78 infected individuals for the facial recognition system, compared to 36 infected individuals for the fingerprint recognition system over a period of 216 days. The results show that the fingerprint recognition system is more efficient than the facial recognition system, according to the SIR model without demographic formulation.

FINGERPRINT RECOGNITION SYSTEM

This project is to design a fingerprint recognition system for security purposes. It will also explore and suggest some solution to the improvement to the existing fingerprint system. Security system that uses a pin code or access card can be easily misused or mishandled. A pin code can be cracked using some hacker software while an access card can easily be stolen or misplaced. Thus, these security methods are very vulnerable to hackers and criminals. Instead, a fingerprint is unique to every person and due to the fact that no two people have the same fingerprint pattern, it makes the fingerprint a very good resource in a security system. The aim of this project is to focus on the concept and methodology of the fingerprint recognition system. By grasping the concept and method of the fingerprint recognition flow, a prototype is developed that will compare an input fingerprint with its predefined template. The system should be able to compare and decide if the input fingerprint is the same as the predefined template. The output of the first stage is a preprocessing stage. There are two stages involved in preprocessing which is the image enhancement and image skeletonization. Fourier transfonn and histogram equalization is utilized to enhance the low quality image to a better image so that the feature extraction process will run smoothly. The second stage of the project is to define the orientation, ROI extraction and minutia extraction. The matching sequence and the angle orientation problem were resolved.

Using the characteristic search algorithm in a library fingerprint identification system

As an important identification method, fingerprint recognition has a wide range of applications. To make the fingerprint recognition system of a library more efficient and secure, a recognition technology based on the characteristic search algorithm is proposed, and the performance of the algorithm is analysed. When a reasonable threshold is set, the matching error rate of the algorithm can be controlled at a lower level, and the algorithm can also ensure a higher fingerprint recognition accuracy and determine the overall accuracy. At the same time, three other identification algorithms of the same type are introduced: radio-frequency fingerprinting, convolutional neural network and local binary pattern. In a comparative experiment, it was found that the characteristic search algorithm model had the highest accuracy, with a value of 94.8%. When dealing with the same amount of fingerprint data, the recognition time of the algorithm model was the shortest. In addition, the area under the curve value corresponding to the receiver operating characteristic curve of the algorithm was the largest, and its value was 0.94. It is well known that the performance of the characteristic search algorithm is optimal and can effectively improve the operation efficiency of a library fingerprint identification system.

Machine Learning Based Non Live Finger Print Detection

In recent days, liveness detection of finger print image has become very essential in finger print recognition systems because fake finger prints are used in lieu of real finger prints. Many machine learning(ML) techniques have been widely used for non live finger print image detection because these techniques provide high accurate identification and also cost effective. These techniques also enhance the accuracy of classification of real and spoof finger print images. In this article , literature review is done about machine learning (ML) and its algorithms used for the detection of non live finger print. The main objective of this article is to compare and analyse various ML techniques used for spoof detection. It also provides an overview of performance merits and limitations of ML algorithms used in non live finger print detection . Index Terms— Non live finger print, liveness detection , Machine learning, anti spoofing, spoof detection

Representation of fingerprint recognition system based on geometric and statistical features of distance and angle of minutiae points

Representation of fingerprint recognition system based on geometric and statistical features of distance and angle of minutiae points

Artificial Multisensory Neuron with a Single Transistor for Multimodal Perception through Hybrid Visual and Thermal Sensing.

An artificial multisensory device applicable to in-sensor computing is demonstrated with a single-transistor neuron (1T-neuron) for multimodal perception. It simultaneously receives two sensing signals from visual and thermal stimuli. The 1T-neuron transforms these signals into electrical signals in the form of spiking and then fires them for a spiking neural network at the same time. This feature makes it feasible to realize input neurons for multimodal sensing. Visual and thermal sensing is achieved due to the inherent optical and thermal behaviors of the 1T-neuron. To demonstrate a neuromorphic multimodal sensing system with the artificial multisensory 1T-neuron, fingerprint recognition, widely used for biometric security, is implemented. Owing to the simultaneous sensing of heat as well as light, the proposed fingerprint recognition system composed of multisensory 1T-neurons not only identifies a genuine pattern but also judges whether or not it is forged.

A hierarchical heterogeneous ant colony optimization based fingerprint recognition system

Personal identification is crucial to secure data against cyber-attacks. With increasing identity theft, fingerprint recognition systems have a growing importance in enforcing security and reliable identification. Although, most fingerprint recognitions systems use minutiae features for fingerprint matching, they require extensive pre-processing of fingerprints when the image quality is poor. This may introduce false ridge patterns, degrading the performance of the system. Moreover, fingerprint matching over a large database can be inefficient due to high computation time of fingerprint matching algorithms. This demand for fingerprint recognition systems that are fast and reliable. This paper proposes a computationally intelligent fingerprint recognition system that extracts ridge patterns from the fingerprint for matching. Hierarchical Heterogeneous Ant Colony Optimization based Fingerprint Matching (HHACOFM) algorithm has ant agents at different levels in the hierarchy to find a match between the input and stored ridge patterns. The algorithm was evaluated over four databases: a synthetic database generated using SFinGe tool, an internal database, SOCOFing database and FVC2004 database. Experimental results indicate that the proposal achieves high recognition rate compared to the existing approaches. HHACOFM algorithm achieves less EER than the state-of-art approaches. The results were validated using statistical tests. HHACOFM enables parallelism and thus reduces the response time. The proposal is scalable and suitable for real time applications demanding fast fingerprint verification.

An Automatic Deep Neural Network Model for Fingerprint Classification

The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving 99% accuracy then it stops and outputs the result. Moreover, this paper proposes an end-to-end methodology for recognizing a person’s identity from the input fingerprint image based on a residual convolutional neural network. It is a complete system and is fully automated whether in the features extraction stage or the classification stage. Our goal is to automate this fingerprint recognition system because the more automatic the system is, the more time and effort it saves. Our model also allows users to react by inputting the initial values of these parameters. Then, the model updates itself until it finds the optimal values for the parameters and achieves the best accuracy. Another advantage of our algorithm is that it can recognize people from their thumb and other fingers and its ability to recognize distorted samples. Our algorithm achieved 99.75% accuracy on the public fingerprint dataset (SOCOFing). This is the best accuracy compared with other models.

Sweat Gland Extraction From Optical Coherence Tomography Using Convolutional Neural Network

As the Level 3 features of fingerprint, sweat pores have attracted attention in the field of fingerprint recognition and have been successfully applied to automatic fingerprint recognition systems. Traditional surface sweat pores become unclear or disappeared when the finger is contaminated, dried, or damaged. These unstable factors create major challenges in collecting sweat pores. Subcutaneous sweat glands belong to the internal tissues of fingers, which are stable and immune to external disturbances. This study investigated the extraction of subcutaneous sweat glands from fingertip volume data collected by optical coherence tomography (OCT). First, an improved multitask V-Net is proposed to extract subcutaneous sweat glands from OCT volume data. The network has an encoding path for features extraction and two decoding paths for extracting sweat gland boundaries and regions, respectively. The multitask scheme is designed to enhance the boundary and shape information of sweat glands and to prevent false extraction caused by interference from other tissues. Second, three mapping methods are proposed to address the problem of different spatial orientations of sweat glands. These three mapping methods, namely, global direct mapping (GDM), local direct mapping (LDM), and cylindrical fitting mapping (CFM), are used to map sweat glands to the surface fingerprint. Experiments are conducted in terms of sweat gland extraction, mapping, and matching. The qualitative and quantitative results show that the proposed network for sweat glands extraction outperforms other methods and that the LDM and CFM methods derive more accurate positions of sweat glands on the surface fingerprint than GDM. In the matching experiment, the equal error rate (EER) of dual-decoding V-Net (DDVN) reached 0.58%, which verified the recognition ability of sweat glands and the effectiveness of the proposed network.

Fingerprint matching using the onion peeling approach and turning function

Fingerprint, as one of the most popular and robust biometric traits, can be used in automatic identification and verification systems to identify individuals. Fingerprint matching is a vital and challenging issue in fingerprint recognition systems. Most fingerprint matching algorithms are minutiae-based. The minutiae points are the ways that the fingerprint ridges can be discontinuous. Ridge ending and ridge bifurcation are two frequently used minutiae in most fingerprint matching algorithms. This article presents a new minutiae-based fingerprint matching using the onion peeling approach. In the proposed method, fingerprints are aligned to find the matched minutiae points. Then, the nested convex polygons of matched minutiae points are constructed and the comparison between peer-to-peer polygons is performed by the turning function distance. Simplicity, accuracy, and low time complexity of the onion peeling approach are three important factors that make it a standard method for fingerprint matching purposes. The performance of the proposed algorithm is evaluated on the database FVC2002. Since the fingerprints that the difference between the number of their layers is more than 2 and the a minutiae matching score lower than 0.15 are ignored, better results are obtained. KeywordsFingerprint Matching, Minutiae, Convex Layers, Turning Function, Computational Geometry.

In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Fingerprint Presentation Attack Detector Using Global-Local Model.

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This article thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module; 2) the local module; and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ~10% in terms of TDR (91.19% versus 80.74%).

An effective latent fingerprint enhancement and recognition system using dictionary learning and LCPnet mechanisms

Latent fingerprint recognition plays an essential role for law enforcement agencies to detect criminals and security purposes. One of the key stages utilized in the latent fingerprint recognition model is to automatically learn consistent minutiae from fingerprint images. However, the existing state-of-the-art recognition approaches are not adequate since live-scan fingerprint images and enhancements are necessary for each step of the recognition process. Hence, an automated recognition system along with appropriate minutiae learning algorithm is required for matching the latent fingerprint exactly. In this paper, an efficient recognition system using dictionary learning and Local Context-Perception deep neural network (LCPnet) has been proposed to enhance the accuracy of latent fingerprint recognition. Primarily, the Total Variation decomposition model is utilized to remove the smooth background noise and dictionary learning contributes to the extraction of multiple patches. Afterward, the LCPnet is trained for 12 patch types to develop a salient minutiae descriptor where every descriptor is trained using LCPnet with a particular patch size at a location surrounding the minutiae. The proposed detection system has been tested through two latent public datasets. Here, three different types of templates (LCPnet minutiae, LCPnet texture, and LCPnet minutiae+texture) are analyzed for evaluating the proposed fingerprint detection system. The performance results manifest that the proposed system acquires a superior recognition accuracy of 99.44% and 99.58% under two different datasets.

COMPARATIVE STUDIES BETWEEN A FACIAL RECOGNITION SYSTEM AND A FINGERPRINT RECOGNITION SYSTEM FOR ACCESS CONTROL

This paper investigates the decisions made on two singles modal biometric systems based on facial recognition and fingerprints for access control. In order to carry out this study on the access control system based on fingerprint and facial recognition, we made biometric recognition systems based on fingerprints and facial recognition that interacts with an embedded system under Arduino to give us the possibility to assemble the performances of programming and electronics, more precisely, we programmed electronic systems for the automatic opening of doors without the action of a human being. In a sample of one hundred individuals composed of 40 females and 60 males, 75 of whom were enrolled and 25 of whom were not, our two singles modal access control systems each obtained the following results: 70 true positives, 5 false negatives, 8 false positives and 17 true negatives for the facial recognition system, compared to 73 true positives, 2 false negatives, 4 false positives and 21 true negatives for the fingerprint recognition system, which constitute our confusion matrix. From all the tests carried out, we can say that the decisions made by the fingerprint recognition system are better than the decisions made by the facial recognition system.

Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features

Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%.

SynCoLFinGer: Synthetic contactless fingerprint generator

We present the first method for synthetic generation of contactless fingerprint images, referred to as SynCoLFinGer. To this end, the constituent components of contactless fingerprint images regarding capturing, subject characteristics, and environmental influences are modeled and applied to a synthetically generated ridge pattern using the SFinGe algorithm. The proposed method is able to generate different synthetic samples corresponding to a single finger and it can be parameterized to generate contactless fingerprint images of various quality levels. The resemblance of the synthetically generated contactless fingerprints to real fingerprints is confirmed by evaluating biometric sample quality using an adapted NFIQ 2.0 algorithm and biometric utility using a state-of-the-art contactless fingerprint recognition system.

A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network

Automatic fingerprint recognition has received significant attention because of its excellent fingerprint stability characteristics. Fingerprints will now explode in popularity as online stores allow payments and secure smartphones. The main objective of this study is to develop a fingerprint recognition system to identify individuals by using a new method, termed as ‘Multi-Connection Hopfield Neural Network’. This might pave the way for the development of a more robust fingerprint recognition system that is more accurate and capable of working in noisy environments. Furthermore, such a system makes operations less complex and saves memory by employing additional tranches of auto-associative memory. This study uses three databases, namely FVC-2004, International NIST-4, and internal database, containing 80, 2000, and 2000 fingerprint images. The proposed fingerprint recognition system achieves 99.65% accuracy without noise. This study also shows that the proposed system works well in the presence of noise and achieves 96.07% accuracy in the presence of 50% random noise.

Finger Print Authentication on Web-Services One Step to Next Security

The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%).

Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet)

Fingerprints are gaining in popularity, and fingerprint datasets are becoming increasingly large. They are often captured utilizing a variety of sensors embedded in smart devices such as mobile phones and personal computers. One of the primary issues with fingerprint recognition systems is their high processing complexity, which is exacerbated when they are gathered using several sensors. One way to address this issue is to categorize fingerprints in a database to condense the search space. Deep learning is effective in designing robust fingerprint classification methods. However, designing the architecture of a CNN model is a laborious and time-consuming task. We proposed a technique for automatically determining the architecture of a CNN model adaptive to fingerprint classification; it automatically determines the number of filters and the layers using Fukunaga–Koontz transform and the ratio of the between-class scatter to within-class scatter. It helps to design lightweight CNN models, which are efficient and speed up the fingerprint recognition process. The method was evaluated two public-domain benchmark datasets FingerPass and FVC2004 benchmark datasets, which contain noisy, low-quality fingerprints obtained using live scan devices and cross-sensor fingerprints. The designed models outperform the well-known pre-trained models and the state-of-the-art fingerprint classification techniques.