Fingerprint Verification System Research Articles

Fingerphoto morphing attack generation using texture descriptors based landmarks

Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusi

Balancing Accuracy and Error Rates in Fingerprint Verification Systems Under Presentation Attacks With Sequential Fusi

Cryptographic Security Approach for Biometric Verification System

This paper presents cryptography which is the science of encryption and decryption to protect fingerprint that can be transmitted from sender to receiver. This security approach (cryptography) can also be applicable to other biometric traits like face, iris, retina and palm print. The significant of this protective medium is to prevent intruders or attacker to easily perceive the presence of fingerprint images. The method that was utilized for implementation of this cryptographic security approach for fingerprint verification System was achieved through Elliptic Curve Cryptography and Hill Cipher Algorithm. An elliptic Curve Function was defined and its domain parameters were used to generate self invertible key matrix that was used for the encryption and decryption process of the biometric images (Fingerprint, Face and Ear ) The security approach was also improved by ensuring that the decryption can only be done through secret key. After decryption, fingerprint verification was done by extracting and matching distinct feature (ridges) from template fingerprint and distinct feature from input fingerprint. The result obtained from demonstration of the cryptographic approach allows end users to load fingerprint image, encrypt it at sending end. At receiver end, end user can decrypt the fingerprint image. Image enhancement was later done before authentication through extraction and matching of distinct features. This model will prevent destruction and manipulation of stored fingerprint image. Authentication can also be accomplished through biological traits instead of use of password that can be stolen or transferred to relative.

Sistema de verificación de huellas dactilares basado en DWT, extracción de características de múltiples dominios y clasificador subespacial de conjuntos

This paper describes a fingerprint verification system including preprocessing, Wavelet transform, feature extraction using multiple domains, and ensemble subspace discriminant classifier. The system is implemented in MATLAB using Wavelet Toolbox, Image Processing Toolbox, and Statistics and Machine Learning Toolbox. First, the motivation and novelty, followed by the review of the previous work, are presented. Next, all steps are described in detail. Three fingerprint databases from the literature are used. The proposed method’s performance is compared with state-of-the-art techniques based on different classifiers utilizing the accuracy metric. The proposed algorithm achieves high accuracy at 97.5% for the DB3-FVC2000 subset.

A Behavioral-Based Fingerprint Liveness and Willingness Detection System

Fingerprints have been used for decades to verify the identity of an individual for various security reasons. Attackers have developed many approaches to deceive a fingerprint verification system, ranging from the sensor level, where gummy fingers are created, to gaining access to the decision-maker level, where the decision is made based on low matching criteria. Even though fingerprint sensor-level countermeasures have developed advanced metrics to detect any attempt to dupe the system, attackers still manage to outwit a fingerprint verification system. In this paper, we present the Micro-behavioral Fingerprint Analysis System (MFAS), a system that records the micro-behavior of the user’s fingertips over time as they are placing their fingerprint on the sensor. The system captures the stream of ridges as they are formed while placed on a sensor to combat the attacks that deceive the sensor. An experiment on 24 people was conducted, wherein the fingerprints and the behavior of the fingertip as it is placed were collected. Subsequently, a gummy finger was created to try to fool the system. Further, a legitimate user was chosen to participate in an experiment that mimicked an attempt to use their fingertip unwillingly to detect coerced fingerprint placement. After applying the micro-behavior, the system reported 100% true positives and 0% false-negatives when providing legitimate vs. gummy-based fingerprints to authenticate a malicious user. The system also reported a 100% accuracy in differentiating between a voluntary and a coerced fingerprint placement. The results improve the fingerprint robustness against attacks on a fingerprint sensor by factoring in micro-behavior, thus helping to overcome fake and coerced fingerprint attacks.

Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning

The fingerprint is one of the leading biometric modalities that is used worldwide for authenticating the identity of persons. Over time, a lot of research has been conducted to develop automatic fingerprint verification techniques. However, due to different authentication needs, the use of different sensors and the fingerprint verification systems encounter cross-sensor matching or sensor interoperability challenges, where different sensors are used for the enrollment and query phases. The challenge is to develop an efficient, robust and automatic system for cross-sensor matching. This paper proposes a new cross-matching system (SiameseFinger) using the Siamese network that takes the features extracted using the Gabor-HoG descriptor. The proposed Siamese network is trained using adversarial learning. The SiameseFinger was evaluated on two benchmark public datasets FingerPass and MOLF. The results of the experiments presented in this paper indicate that SiameseFinger achieves a comparable performance with that of the state-of-the-art methods.

Biometric based Fingerprint Verification System for ATM machines

In this modern world, almost everyone uses ATM machines which allow people to transfer and withdraw cash. This study is based on executing a fingerprint method in the ATM System. We chose this field to improve safety and security for people to make the transaction easier. The fingerprints are unique for each person. There is no insecurity of losing an ATM card and no requirement to carry an ATM card with you every time. On comparison of different technologies for ATM security, the fingerprint technology operates better and safer than others. These reasons make this mechanism an effortless and secure way of transaction and also maintains a coherent ambience with users and ATM machines. This is the most latest technology in electronic cash transactions.

Retraction: Biometric based Fingerprint Verification System for ATM machines (J. Phys.: Conf. Ser. 1916 012033)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation.IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process.IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further.[1] Cabanac G, Labbé C and Magazinov A 2021 arXiv:2107.06751v1 Retraction published: 23 February 2022

Fingerprint liveness detection through fusion of pores perspiration and texture features

Spoofing attacks on the fingerprint scanners become major and serious concern with the growing development and use of biometric technologies. Level 1 and Level 2 features; which are said to be unique and most commonly used features in fingerprint verification systems, are easily get spoofed. Moreover, single feature based specifically designed spoof detection methods are not performed well on different fingerprint scanners and spoofing materials. This paper proposed the fusion of pores perspiration and texture features in static software based approach to identify live and fake fingerprints. The pores perspiration activity is quantified by computing the ridge signal energy and gray level distributions around the detected pores. These pore characteristics are statically determined instead of dynamic measurement. Autoencoder neural network is used to reduce the high dimensional feature vector and learn its low dimensional hidden representation unsupervisedly. The binary classification in two classes: live and spoof is performed by the supervisedly trained softmax classifier. The performance of the classifier is evaluated in terms of Average Classification Error (ACE) and misclassification rates: FerrLive and FerrFake. The experimental results carried out on LivDet 2013 and LivDet 2015 databases show the improvement of the classifier performance in comparison to the state-of-the-art methods.

Fingerprint Biometric System Hygiene and the Risk of COVID-19 Transmission

Biometric systems use scanners to verify the identity of human beings by measuring the patterns of their behavioral or physiological characteristics. Some biometric systems are contactless and do not require direct touch to perform these measurements; others, such as fingerprint verification systems, require the user to make direct physical contact with the scanner for a specified duration for the biometric pattern of the user to be properly read and measured. This may increase the possibility of contamination with harmful microbial pathogens or of cross-contamination of food and water by subsequent users. Physical contact also increases the likelihood of inoculation of harmful microbial pathogens into the respiratory tract, thereby triggering infectious diseases. In this viewpoint, we establish the likelihood of infectious disease transmission through touch-based fingerprint biometric devices and discuss control measures to curb the spread of infectious diseases, including COVID-19.

Shift, rotation and scale invariant optical fingerprint verification system with double random phase encoding

Biometric features such as fingerprint images can be authenticated by not only computer algorithms but also optical systems. In previous works, double random phase encoding (DRPE) optical lens system is employed for fingerprint verification. However, such a system can yield authentication failure when the key is generated from a slightly distorted fingerprint image. In this work, a fingerprint image pre-processing scheme for DRPE fingerprint optical authentication system is proposed. Simulated experimental results demonstrate the proposed method has significantly better robustness to shift, rotation and scale variance than some of the conventional DRPE-based approaches.

Low Resolution Fingerprint Image Verification using CNN Filter and LSTM Classifier

A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. One of the main biometric system is fingerprint recognition system. The reduced rate of performance of fingerprint verification system is due to many reasons such as displacement of finger during scanning, moisture on scanner, etc. The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. According to literature several Fingerprint Recognition System are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. This research work is focused on designing of fingerprint verification/classification including feature extraction methods and learning models for proper classification to label different fingerprints. In order to gain above mentioned objectives, FVC2002 dataset is taken for training and testing. In this dataset there are approx. 72 images which are used for testing purpose. In this dataset there are some blur, distorted as well as partial images also which are considered for recognition. Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) is used for recognition of fingerprint. The result analysis shows approx. 3% enhancement over existing work.

Bio Key Generation for Integrity Checking Using Elliptic Curves in Fingerprint Verification System

This paper proposes a Bio-key generation method for integrity checking in fingerprint verification system. A major drawback in traditional cryptography is random key generation and those keys need to be remembered by the user. To overcome such drawback the crypto-Bio-key generation system is introduced. In this system, there are three process to follow. Initially, a revocable transformation of the compressed fingerprint feature data using transformation key for security enhancement. Secondly, the key exchange technique using Elliptic curve cryptography (ECC) to generate secret key. Finally SHA256 hash function generates the Bio-key for high level integrity checking.

Optimisation of Point-Set Matching Model for Robust Fingerprint Verification in Changing Weather Conditions

Aims: To provide a baseline for the configuration of Automated Fingerprint Verification System (AFVS) in the face of changing weather and environmental conditions in order to ensure performance accuracy. 
 Study Design: Statistical and theoretical research approaches.
 Place and Duration of Study: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, between July 2017 and July 2018.
 Methodology: Data set were collected in the South-South geopolitical zone of Nigeria. We use 10,000 minutiae points defined by location and orientation features extracted from fingerprint samples obtained at 9 various physical and environmental conditions over 12 months period. These data were used to formulate linear regression models that were used as constraints to the verification objective function derived as constrained linear least squares. The effects of the changing weather and environmental conditions were incorporated into the optimised point-set matching model in order to minimise the total relative error on location and orientation differences between pairs of minutiae. The model was implemented using interior-point convex quadratic programming was implemented in Matlab.
 Results: The results obtained from the optimisation function by adjusting the thresholds of the effects of weather and environmental conditions to 0.0, 0.0 for location and orientation properties of minutiae, respectively, showed minimal total relative errors on the corresponding pairs of matched minutiae, when compared with using the default threshold values of the selected conditions.
 Conclusion: The optimisation of point-set based model could provide a computational basis for accurate fingerprint verification for low and high-security AFVS in unfavourable conditions if they are incorporated into the matching model. However, further validation and evaluation of the model with data sets from regions with similar weather and environmental conditions is needed to further validate its robustness in terms of performance accuracy

Fixed-length asymmetric binary hashing for fingerprint verification through GMM-SVM based representations

Fingerprint minutiae information is an unordered and variable-sized collection of minutiae locations and orientations. Advanced template protection algorithms which require a fixed-length binary template cannot operate on minutiae points. In this paper, we propose a novel framework that provides practical solutions that can be used in developing secure fingerprint verification systems. The framework, by using a GMM-SVM fingerprint representation scheme, first generates fixed-length feature vectors from minutiae point sets. The fixed-length representation enables the application of modern cryptographic alternatives based on homomorphic encryption to minutiae template protection. Our framework then utilizes an asymmetric locality sensitive hashing (ALSH) in order to convert the generated fixed-length but real valued GMM-SVM feature vector to a binary bit string. This binarization step transforms the matching process to calculating Hamming distance between binary vectors and expedites fingerprint matching. The verification performance of the framework is evaluated on FVC2002DB1A and DB2A databases.

A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification

In spite of the benefits of biometric-based authentication systems, there are few concerns raised because of the sensitivity of biometric data to outliers, low performance caused due to intra-class variations and privacy invasion caused by information leakage. To address these issues, we propose a hybrid fusion framework where only the protected modalities are combined to fulfill the requirement of secrecy and performance improvement. This paper presents a method to integrate cancelable modalities utilizing mean-closure weighting (MCW) score level and Dempster-Shafer (DS) theory based decision level fusion for iris and fingerprint to mitigate the limitations in the individual score or decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the similarity scores from different matchers corresponding to each protected modality. The individual scores obtained from different matchers for each modality are combined using MCW score fusion method. The MCW technique achieves the optimal weight for each matcher involved in the score computation. Further, DS theory is applied to the induced scores to output the final decision. The rigorous experimental evaluations on three virtual databases indicate that the proposed hybrid fusion framework outperforms over the component level or individual fusion methods (score level and decision level fusion). As a result, we achieve (48%,66%), (72%,86%) and (49%,38%) of performance improvement over unimodal cancelable iris and unimodal cancelable fingerprint verification systems for Virtual_A, Virtual_B and Virtual_C databases, respectively. Also, the proposed method is robust enough to the variability of scores and outliers satisfying the requirement of secure authentication.

IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH

Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of [Formula: see text]%, which outperformed the compared methods. The comparative results imply the statistical superiority ([Formula: see text]) of the proposed approach compared to the counterparts.

Fingerprint Verification System based on Active Forgery Techniques

Fingerprint Verification System based on Active Forgery Techniques

A Conceptual Study on Fingerprint Thinning Process based on Edge Prediction

Biometric recognition encompasses numerous modern strategies. Among them, fingerprint reputation is taken into consideration to be the most effective approach for utmost security authentication. As industrial incentives boom, many new technologies for user identity are being advanced, each with its very own strengths and weaknesses and a potential area of interest marketplace. Fingerprint matching consists of a different process like filtering or preprocessing, binarisation, thinning or skeletonisation, postprocessing, feature extraction, and matching. Out of these fingerprint thinning or skeletonisation is one of the important processes in fingerprint identification or verification systems. Fingerprint thinning or skeletonisation is the manner or technique of lowering the thickness of every line of a fingerprint pattern or ridge pattern to just a single pixel width. After extracting the minutiae from the improved, binarised and thinned image some post-processing is carried out on this final fingerprint image to take away any spurious minutiae. The techniques on this class are of types–crossing number based and morphology-based totally. In this paper even though a new method for thinning is not proposed but a real attempt is made to explain the Edge prediction based thinning process. The Edge Prediction based Skelton formation is totally based on the conditional thinning set of rules, which is used to carry out thinning. The Edge Prediction based thinning process is explained with the help of workflow, algorithm, and flowchart.

Fingerprint Matching through Back Propagation Neural Network

Objectives: This paper is focused with original features used in fingerprint matching founded on back propagation neural network. For finding the features in the images, it can be improved using filtering techniques as isotropic and anisotropic. Isotropic can protect features on the input images but can barely progress the dominance of the images. But on the contrary, anisotropic filtering can successfully eliminate noise from the image just when a consistent point of reference is provided. Methods and Analysis: The filters commonly used namely median filter, gabor filter along with anisotropic filters are used for filtering the noises under direct gray scale enhancement .Whereas for an input image, the narrow ridge direction is predictable and the region of interest is positioned. The uniqueness of a fingerprint is exclusively determined by the local ridge characteristics and their relationships. The ridges and valleys in a fingerprint alternate, flowing in a local constant direction. The two most prominent local ridge characteristics are: 1) ridge ending and, 2) ridge bifurcation. A ridge ending is defined as the point where a ridge ends rapidly. A ridge bifurcation is defined as the point where a ridge forks or diverges into branch ridges. Collectively, these features are called minutiae. Minutiae points are extracted during the enrollment process and then for each authentication. In a fingerprint, they correspond to either a ridge ending or a bifurcation. Minutiae are major features of a fingerprint using which comparisons of one print with another can be made. After finding the minutiae points the filters reducing image noises, smoothing, removing some forms of misfocus and motion blur, is in the front step of image processing. Filtering is also used for preserving the true ridge and valley structures. Findings: The digital results of these features are practically feeded as input of the neural network using median filter, gabor filter, anisotropic filter for training function. For fingerprint identification the confirmation part of the system identifies the fingerprint based training show of the network. Novelty and Improvement: To finish the new outcome reveals that the number of accepted sample rate of the proposed method using the three filters which is far better than the existing fingerprint verification system using artificial neural network