Biometric Identifier Research Articles

Accurate 3D Finger Knuckle Recognition Using Auto-Generated Similarity Functions

Contactless 3D finger knuckle is an emerging biometric identifier, which can provide a promising alternative for personal identification. To maximize its potential, feature representation and matching are the two critical components towards high performance. A recent pioneering work is limited by its preliminary design of the feature descriptor and the tailor-made similarity function. Although this method demonstrates decent recognition performance, there is room for improvement. This article advances the state-of-the-art method by introducing a new curvature based feature descriptor and a method to compute the similarity functions based on the statistical distribution of the encoded feature space. Our proposed feature representation utilizes an insight in 3D geometry for accurately encoding the curvature information. When computing the similarity between a pair of templates, we compute the similarity function from the probability mass distributions of the encoded feature space. Our proposed approach is scalable to templates with different sizes, and more importantly outperforms the state-of-the-art methods significantly, which is demonstrated in a publicly available database of 3D finger knuckle. In addition, we also demonstrate the generalizability of our approach by evaluating on other publicly available biometric datasets of similar patterns, i.e., 3D palmprint and finger vein.

Finger knuckle print recognition for personal authentication based on relaxed local ternary pattern in an effective learning framework

Finger knuckle print (FKP) as a physiological trait with a small image dimension, also a highly distinctive pattern, can be used as a reliable biometric identifier. In this paper, a new effective biometric authentication system using FKP texture based on relaxed local ternary pattern (RLTP) is presented. To further improve performance, cascading, overlapped patching and uniform rotation invariant pattern selection are used. Also to obtain more discriminative dominant patterns, an efficient learning framework is integrated with RLTP feature vectors. Identification and verification experiments conducted on the standard PolyU FKP dataset show the effectiveness of the proposed scheme.

Controllable Key Agreement With Correlated Noise

The problem of secret-key-based authentication under privacy and storage constraints on the source sequence is considered. Identifier measurement channels during authentication are assumed to be controllable via a cost-constrained action sequence. Inner and outer bounds for the key-leakage-storage-cost regions are derived for a generalization of the classic two-terminal key agreement model. Additions to the model are that the encoder observes a noisy version of a remote source, and this noisy output and the remote source output together with an action sequence are given as inputs to the measurement channel at the decoder. Thus, correlation is introduced between the noise components on the encoder and decoder measurements. The model with a key generated by an encoder is extended to the randomized models, where a key is embedded to the encoder. The results are relevant for several user and device authentication scenarios including physical and biometric identifiers with multiple measurements that provide diversity and multiplexing gains. Achievable (key, storage, cost) tuples are evaluated for binary identifiers and measurement channels represented as a mixture of binary symmetric subchannels. Significant gains from using an action sequence are illustrated to motivate the use of low-complexity transform-coding algorithms with cost-constrained actions.

Concurrent Two-Factor Identify Verification Using Facial Identify and Facial Actions

Identity verification is ubiquitous in daily life. Its applications range from unlocking mobile device to accessing online account, boarding airplane or other types of transportation, recording times of arrival and leaving work, controlling access to a restricted area, facility, or vault, and many more. The traditional and the most popular identity verification is password authentication but with many challenges. Human biometric identifiers like fingerprint, retina scan, and 2D or 3D facial features have become popular alternatives. Some applications use two-factor or multi-factor authentication to increase system security, e.g., password and login code sent to a mobile device. All these identity verification methods have their challenges ranging from forgotten or stolen password to unaware or unintentional authentication and complexity and high costs. This paper presents a promising alternative that could be an improvement to the existing identity verification methods. This improved identity verification is a two-factor approach that concurrently analyzes facial features and unique facial actions. The user's facial features and facial actions must both match what have been stored in the system in order to pass identity verification. This two-factor verification requires only the frontal view of the face and authenticates facial features and facial actions concurrently. It generates an embedding of facial features and facial action in a short video for matching. We name this method Current Two-Factor Identity Verification (C2FIV). Two frameworks that use recurrent neural networks to learn the representation of facial features and actions. One uses an auto-encoder, and the other one uses metric learning. Experimental result shows that the metric learning model performs reliably with an average precision of 98.8%.

Learning deep features for task-independent EEG-based biometric verification

Abstract Considerable interest has been recently devoted to the exploitation of brain activity as biometric identifier in automatic recognition systems, with a major focus on data acquired through electroencephalography (EEG). Several researches have in fact confirmed the presence of discriminative characteristics within brain signals recorded while performing specific mental tasks. Yet, to make EEG-based recognition appealing for practical applications, it would be highly advisable to investigate the existence and permanence of such distinctive traits while performing different mental tasks. In this regard, the present study evaluates the feasibility of performing task-independent EEG-based biometric recognition. A deep learning approach using siamese convolutional neural networks is employed to extract, from the considered EEG recordings, subject-specific template representations. An extensive set of experimental tests, performed on a multi-session database comprising EEG data acquired from 45 subjects while performing six different tasks, is employed to evaluate whether it is actually possible to verify the identity of a subject using brain signals, regardless the performed mental task.

Palm Vein Technology

Security has become one of the key concerns in our day to day life. Personal identification technology is becoming popular now-a –days. Personal identificationsare not guessable, cannot be reproduced easily. They are fingerprint, iris, retina,veins and these are called as Biometric identifiers. Of all these biometric identifying techniques “Palm vein authentication technology” has the highest accuracy, false rejection rate of 0.01% and false acceptance rate of 0.00008% or lower and this is based on research done by Fujitsu over 140,000 palms.The Palm vein Technology uses veins in palms as authentication feature. The palm is placed over palm vein sensor and IR rays are emitted by the sensor ,the deoxygenated haemoglobin in veins absorbs the IR rays and the veins are recorded as dark lines .IR rays are being used because deoxygenated haemoglobin absorbs light in range of 7.6*10^(-4)mm. This type of method is called a reflection method. This technology is not only limited to login authentication but also physical access control systems, payment systems, financial services, healthcare and airport security. In the present an attempt was made to narrate and explain the uses of palm vein technology, how it is being implemented, why it is safer than other biometric identifying technologies.

Ear biometric verification approach based on morphological and geometric invariants

<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states. Experimental results using several trails verified that the proposed approach is achieved high accuracy level over a wide variety of ear images. Also, the verification process will be completed by matching query ear image with ear images that kept in database during real time.</p>

Deep Feature Collaboration for Challenging 3D Finger Knuckle Identification

Contactless 3D finger knuckle pattern is a new biometric identifier which offers highly discriminative features for the finger knuckle based personal identification. State-of-the-art methods for object recognition, a more generic problem, employ deep neural network based approaches and demonstrate superior effectiveness. However, any direct applications from those methods do not outperform specialized hand-crafted feature description approaches for the problem addressed in this paper. In addition, such deep neural network based methods have to address challenges associated with emerging biometrics, e.g. availability of very limited training data, large intra-class or train-test sample variations as observed for the real applications, etc. This paper attempts to address the above challenges and introduces a new deep neural network based approach for the contactless 3D finger knuckle identification. Our approach simultaneously encodes and incorporates deep features from multiple scales to form a more robust deep feature representation. Such collaborative feature representations are robustly matched using an efficient alignment scheme with a fully convolutional architecture to accommodate involuntary finger variations during the contactless imaging. Comparative experimental results in the two-session 3D finger knuckle images database, acquired from over 200 subjects and is publicly introduced from this paper, illustrate superior performance over the state-of-the-art methods, e.g. offering ~22% GAR improvement at extremely low FAR under challenging comparison scenarios. Additional experiments in other publicly available databases including 3D palmprint, 3D fingerprint, and 2D finger knuckle further validate the effectiveness and demonstrate the generalizability of the proposed approach.

Iterated graph cut method for automatic and accurate segmentation of finger-vein images

Recent advances in computer vision and machine intelligence have facilitated biometric technologies, which increasingly rely on image data in security practices. As an important biometric identifier, the near-infrared (NIR) finger-vein pattern is favoured by non-contact, high accuracy, and enhanced security systems. However, large stacks of low-contrast and complex finger-vein images present barriers to manual image segmentation, which locates the objects of interest. Although some headway in computer-aided segmentation has been made, state-of-the-art approaches often require user interaction or prior training, which are tedious, time-consuming and prone to operator bias. Recognizing this deficiency, the present study exploits structure-specific contextual clues and proposes an iterated graph cut (IGC) method for automatic and accurate segmentation of finger-vein images. To this end, the geometric structures of the image-acquisition system and the fingers provide the hard (centreline along the finger) and shape (rectangle around the finger) constraints. A node-merging scheme is applied to reduce the computational burden. The Gaussian probability model determines the initial labels. Finally, the maximum a posteriori Markov random field (MAP-MRF) framework is tasked with iteratively updating the data models of the object and the background. Our approach was extensively evaluated on 4 finger-vein databases and compared with some benchmark methods. The experimental results indicate that the proposed IGC method outperforms the state-of-the-practice approaches in finger-vein image segmentation. Specifically, the IGC method, relative to its level set deep learning (LSDL) counterpart, can increase the average F-measure value by 5.03%, 6.56%, 49.91%, and 22.89% when segmenting images from four different finger-vein databases. Therefore, this work can provide a feasible path towards fully automatic image segmentation.

Enhancing Accuracy in a Touch Operation Biometric System: A Case on the Android Pattern Lock Scheme

The main objective of this research study is to enhance the functionality of an Android pattern lock application by determining whether the time elements of a touch operation, in particular time on dot (TOD) and time between dot (TBD), can be accurately used as a biometric identifier. The three hypotheses that were tested through this study were the following–H1: there is a correlation between the number of touch stroke features used and the accuracy of the touch operation biometric system; H2: there is a correlation between pattern complexity and accuracy of the touch operation biometric system; H3: there is a correlation between user training and accuracy of the touch operation biometric system. Convenience sampling and a within-subjects design involving repeated measures were incorporated when testing an overall sample size of 12 subjects drawn from a university population who gave a total of 2,096 feature extracted data. Analysis was done using the Dynamic Time Warping (DTW) Algorithm. Through this study, it was shown that the extraction of one-touch stroke biometric feature coupled with user training was able to yield high average accuracy levels of up to 82%. This helps build a case for the introduction of biometrics into smart devices with average processing capabilities as they would be able to handle a biometric system without it compromising on the overall system performance. For future work, it is recommended that more work be done by applying other classification algorithms to the existing data set and comparing their results with those obtained with DTW.

Finger Vein Verification Techniques using Convolutional Neural Networks

Finger vein beneath our skin is one of the unique features for identifying an individual. Because of its uniqueness and security the finger vein recognition is considered as a powerful biometric identifier for user authentication. Several techniques have been evolved for finger vein recognition from its early stage of development, but majority approaches were based on hand crafted features which had limitations on quality of the image, positioning of the finger etc. The emergence of neural networks led to the development of various Convolutional Neural Networks (CNN) based approaches for identity verification. This paper surveys various finger vein verification techniques using CNN and determines the factors that will affect the final result. Publicly available finger vein datasets as well as user designed ones, which are of different qualities, are used for the experimental analysis of these techniques. Though CNN is used in all the cases each one differs in the number of layers used, weight updating methods, results obtained etc. It is found that higher recognition accuracy and lower equal error rate (EER) makes the finger vein verification system an effective one. This field has emerged wide popularity recently and is used in different applications where security is of prime importance.

Live Scan Plebiscite System

Live scan plebiscite system is an electronic system or device employed in any legal system to cast their votes rather than ballot papers and boxes which used earlier in conventional Election system. The traditional system may be a long, time-consuming process and there’s a clear stage of error and malpractices. Later the system was replaced by electronic voting machine. To overcome the disadvantages of the electronic voting system, biometric identifiers are included in the voting system. Our proposed system consists of a Ballot unit with biometric system. This system verifies the biometrics of the candidate with the already stored data in Aadhar or voter identification card. In the proposed system the voter should verify his/her finger print and only if it matches they can vote.The data of the voters are stored in the cloud and can be retrieved from anyplace, so the voter can vote from any place outside the constituency. In the Electronic voting system, there is a simple unit called ballot unit which is used to record the votes. In this electronic voting only, the votes are recorded and counted and there is no separate unit to verify the voter, so anybody can record the votes and any number of times.

Feature Level Fusion of Face and voice Biometrics systems using Artificial Neural Network for personal recognition

Lately, human recognition and identification has acquired much more attention than it had before, due to the fact that computer science nowadays is offering lots of alternatives to solve this problem, aiming to achieve the best security levels. One way is to fuse different modalities as face, voice, fingerprint and other biometric identifiers. The topics of computer vision and machine learning have recently become the state-of-the-art techniques when it comes to solving problems that involve huge amounts of data. One emerging concept is Artificial Neural networks. In this work, we have used both human face and voice to design a Multibiometric recognition system, the fusion is done at the feature level with three different schemes namely, concatenation of pre-normalized features, merging normalized features and multiplication of features extracted from faces and voices. The classification is performed by the means of an Artificial Neural Network. The system performances are to be assessed and compared with the K-nearest-neighbor classifier as well as recent studies done on the subject. An analysis of the results is carried out on the basis Recognition Rates and Equal Error Rates. Index Terms: Bioinformatiscs, Face, voice, Multibiometric recognition system, fusion at feature level, Artificial Neural Network(ANN).

Efficient and Accurate 3D Finger Knuckle Matching using Surface Key Points.

Contactless 3D finger knuckle is a new biometric identifier with a lot of potentials, which can provide an accurate, efficient and convenient alternative for the personal identification. The current 3D finger knuckle recognition methods are limited by computationally complex or inefficient matching algorithms, which attempt to compute the matching scores from all possible translational and rotational parameters for matching a pair of templates. The strength of such approach lies in its simplicity and reliability for accurately matching intra-class samples, but expensive computational time is required. Furthermore, attempting on excessive numbers of translational and rotational parameters can also degrade the overall recognition accuracy because the imposter matches can be increased. In fact, this conventional matching approach is commonly adopted in many biometric studies, but its drawbacks have not received adequate attention. This paper addresses such 3D finger knuckle recognition problem by developing a more efficient matching approach using surface key points extracted from 3D finger knuckle surfaces. Our comparative experimental results with the state-of-the art method on a publicly available 3D finger knuckle database indicates that our approach can offer over 23 times faster with performance improvement on the accuracy. Although the focus of our work is on 3D finger knuckle recognition, we also present the performance of our method on other publicly available databases with similar 3D biometric patterns including 3D palmprint and 3D fingerprint, to validate the effectiveness of the proposed approach.

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

Nowadays, there are several identification systems which are based on different biometric modalities. In particular, multispectral images of palmprints captured in different spectral bands have a very distinctive biometric identifier. This paper proposes a novel fusion scheme of a biometric recognition system by multiSpectral palmprint. This system is composed of three blocks: (1) extraction of the region of interest (ROI) from multispectral images, (2) a new image fusion architecture based on the measurement of decorrelation, and (3) a scheme of dimension reduction and classification. The proposed image fusion system combines the information from the same left and right spectral band using the 2D discrete wavelet (DWT) transform technique. In addition, a feature extraction using the Log-Gabor transform is performed, while the feature size has been reduced using the Kernel Principal Component Analysis technique (KPCA). In Our experiments we use CASIA multispectral palmprint database. We obtained an accuracy rate (ACC) of 99.50% for the spectral bands WHT (white light) and 940 nm and an equal error rate EER = 0.05%.These results show that our system is robust against spoofing.

A View Transformation Model Based on Sparse and Redundant Representation for Human Gait Recognition.

Background:Human gait as an effective behavioral biometric identifier has received much attention in recent years. However, there are challenges which reduce its performance. In this work we aim at improving performance of gait systems under variations in view angles, which present one of the major challenges to gait algorithms.Methods:We propose employment of a view transformation model based on sparse and redundant (SR) representation. More specifically, our proposed method trains a set of corresponding dictionaries for each viewing angle, which are then used in identification of a probe. In particular, the view transformation is performed by first obtaining the SR representation of the input image using the appropriate dictionary, then multiplying this representation by the dictionary of destination angle to obtain a corresponding image in the intended angle.Results:Experiments performed using CASIA Gait Database, Dataset B, support the satisfactory performance of our method. It is observed that in most tests, the proposed method outperforms the other methods in comparison. This is especially the case for large changes in the view angle, as well as the average recognition rate.Conclusion:A comparison with state-of-the-art methods in the literature showcases the superior performance of the proposed method, especially in the case of large variations in view angle.

An Implementation of Electronic Passport Scheme Using Encrypted Security Along with Multiple Biometrics

Within the next year, travellers from dozens of nations may be carrying a new form of a passport. Electronic passports have known a wide and fast deployment all around the world since the International Civil Aviation Organization the world has adopted standards whereby passports can store biometric identifiers.

Clothing invariant human gait recognition using modified local optimal oriented pattern binary descriptor

Human gait is a behavioral characteristic which has received a large amount of consideration in recent times as a biometric identifier. The clothing variance is one of the most common covariate influences which can influence the performance of gait recognition approach in real-world scenarios. This paper proposes a gait recognition approach proficient in choosing information characteristics for individual identification under different clothing conditions. The proposed method constitutes of addressing the feature extraction technique by introducing a binary descriptor called as Modified Local Optimal Oriented Pattern (MLOOP). In the proposed approach, initially, the feature vectors such as histogram and horizontal width vector are extracted from MLOOP descriptor, and then the dimensionality of the feature vector is reduced to remove the irrelevant features. The performance of MLOOP was accessed against its predecessors. Obtained experimental results demonstrate that the MLOOP descriptor performs better than the previous binary descriptors. Furthermore, the performance analysis of the proposed approach was assessed on OU-ISIR B treadmill gait database and CASIA B gait database. Broad investigations demonstrate the viability of the proposed technique.

Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

Highly secure access control requires Swiss-cheese-type multi-layer security protocols. The use of electroencephalogram (EEG) to provide cognitive indicators for human workload and fatigue has created environments where the EEG data are well-integrated into systems, making it readily available for more forms of innovative uses including biometrics. However, most of the existing studies on EEG biometrics rely on resting state signals or require specific and repetitive sensory stimulation, limiting their uses in naturalistic settings. Moreover, the limited discriminatory power of uni-variate measures denies an opportunity to use dependences information inherent in brain regions to design more robust biometric identifiers. In this paper, we proposed a novel model for ongoing EEG biometric identification using EEG collected during a diverse set of tasks. The novelty lies in representing EEG signals as a graph based on within-frequency and cross-frequency functional connectivity estimates, and the use of graph convolutional neural network (GCNN) to automatically capture deep intrinsic structural representations from the EEG graphs for person identification. An extensive investigation was carried out to assess the robustness of the method against diverse human states, including resting states under eye-open and eye-closed conditions and active states drawn during the performance of four different tasks. We compared our method with the state-of-the-art EEG features, classifiers, and models of EEG biometrics. Results show that the representation drawn from EEG functional connectivity graphs demonstrates more robust biometric traits than direct use of uni-variate features. Moreover, the GCNN can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.

A Face Recognition System Using Directional Binary Code Algorithm And Multi-Svm

Over past few years, face recognition technology plays an important function in the development of biometric identifier with less time consuming and computational overhead. Many researchers were put their effort to develop face recognition algorithm involves three distinct steps such as detection, unique faceprint creation and finally verification. Traditional Local binary pattern based face recognition system slow down the recognition speed, high computational complexity and does not give the directional data of the picture. In order to overcome the above limitation, a novel face recognition system is proposed by employing the advantage of Directional Binary Code (DBC) feature extraction method. The face images features are extracted from DBC are generally smoother than other feature extraction methods. The images with blur creation, pose changes, and illumination is applied and stored in the database. For blur creation various filters such as Average filter, Gaussian filter and Motion filter are used. By using Directional Binary Code method, the face is detected and extracted. Then the same algorithm is used for input images and with help of Multi-SVM classifier multiple images in the database is compared and shows the matched images. Finally, simulation result shows the implemented results in term of its recognition speed and computation complexity.