Biometric Algorithm Research Articles

Biometric Algorithms as Border Infrastructures

Abstract Looking at the work performed by infrastructures when they become part and parcel of the security governance, in this paper, I contend that a closer look must be paid to the infrastructural context of emergence and possibility of algorithms applied in “smart border technologies”. I focus on the explanatory and productive power of an analytical concept derived from the practice: the “security/mobility nexus”, which refers to the stitching of security to mobility to make governance possible. I illustrate how through the security/mobility nexus the Canadian State has capitalized on the promises of infrastructures–such as biometric algorithms–to innovate and deploy the affordance power of the digital to connect people’s data to spaces and physical sites. To analytically reflect on how it comes to mediate bodies as a “border infrastructure” with the security/mobility nexus, I first focus on the algorithmic mediation before turning to the biometric imaginary and its limits.

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

The goal of a biometric system is to recognize individuals based on their unique physiological or behavioral traits. Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals, where they leave a recognizable trail of behavioral information. Social Behavioral Biometric (SBB), being an emerging trend, focuses on such trails to distinguish between individuals. This research investigates the impact of users' writing profiles on OSN to conclude whether such profiles contribute to SBB. The distinctiveness of the SBB features that are extracted from the social behavioral data of Twitter is studied. A person identification system that relies on users' writing profiles, reply, retweet, shared weblink, trendy topic networks and temporal profiles is proposed. Score and rank level weighted fusion algorithm performance is compared on a social interaction database of 241 Twitter users. The experimental results establish that the users' writing profiles have the highest impact over other social biometric features and that score level fusion algorithms perform better than rank level fusion on SBB. The proposed system has achieved recognition rate of 99.45% at rank-1 after cross-validation using genetic algorithm based score level fusion algorithm. The system outperformed all prior researches on SBB in terms of identification accuracy.

Automatic biometric verification algorithm based on the bifurcation points and crossovers of the retinal vasculature branches

Biometric identification systems allow for the automatic recognition of individuals on one or more biometric characteristics. In this paper, we propose an automatic identity verification algorithm based on the structure of the vascular network of the human retina. More precisely, the biometric template consists of the geometric coordinates of bifurcation points and crossovers of the vascular network branches. The main goal of our work is to achieve an efficient system while minimising the processing time and the size of the data handled. Therefore, this algorithm uses a novel combination of powerful techniques for feature extraction based on mathematical morphology, like the watershed transformation for the segmentation of the retinal vasculature and hit-or-miss transform for the detection of bifurcation points and crossovers. We detail each step of the method from acquisition and enhancement of retinal images to signature comparison through automatic registration. We test our algorithm on a retinal images database (DRIVE). Finally, we present and discuss the evaluation results of our algorithm, and compare it with some of the literature.

Palmprint recognition using the cosine method

Palmprint Recognition Technology now requires breakthrough identification of diverse people. Palmprint recognition is the right choice of the system, namely the acceptance of biometrics that can be done quickly and cheaply because it has a significant enough media dimension that is difficult to manipulate. The four classifications of the biometric algorithm, the use of matching methods is still less attractive to researchers. In general, the part of the research concern is in the preprocessing and dimension reduction sections. Although researchers more widely use the Euclidean matching method, the selection of cosine methods is worth considering. The cosine method for the palmprint recognition matching process will have a significant effect on increasing the verification value when inserting the use of the contra-variance formulae in the equation. The selected amount of contra-variance is the data of the training. From the results of research that have been carried out, the rate of EER and verification are quite promising. The value of research results can compensate for other researchers in the same field.

Three-Dimensional Weightbearing Assessment of the First Ray in Hallux Valgus: A Case-Control Study

Category:BunionIntroduction/Purpose:Coronal plane rotational deformity of the first ray has been implicated with the developmental pathologic process of hallux valgus (HV). Weight Bearing CT (WBCT) is an imaging technology that can support the assessment of this complex three-dimensional (3D) deformity. The objective of the study was to analyze the 3D architecture of the first ray in patients with HV when compared to controls using WBCT images and a 3D biometric algorithm analyzing the deformity in all three planes. We hypothesized that WBCT would confirm the rotational deformity in HV patients, and that the 3D algorithm would demonstrate increased specificity and sensitivity for the pathology when compared to traditional two-dimensional (2D) HV measurements such as the 1-2 intermetatarsal angle.Methods:Retrospective case-control study, ethics committee approved. Twenty-one feet of patients with clinically symptomatic HV and 20 feet of asymptomatic controls were included. Exclusion criteria applied were previous trauma or surgery affecting first ray or forefoot morphology. All patients were assessed using WBCT. First ray 3D coordinates (x, y, z) were harvested including: center-points of the heads and bases of the first and second metatarsals, center-point of the medial and lateral sesamoids, distal condyles of the proximal phalanx (PP) of the first toe, as well as the medial and lateral borders of the first metatarsal head and diaphysis. The 2D measurements (dorsoplantar 1-2 intermetatarsal (IMA) and metatarsophalangeal (MPA) angles) were obtained using digitally reconstructed radiographs (DRR). The Sesamoid Rotation Angle (SRA) was measured in the coronal plane. Using these coordinates, all 2D, 3D axes, distances, angulations and 3D biometric for HV (HV-3DB) could be calculated.Results:Mean ages were respectively 62.2y in the HV group and 48.8y in the control group (p<0.05). In 2D, the mean IMA and MPA for HV/controls were respectively 14.9/9.3 (p<0.001) and 30.1/13.1 (p<0.001). The SRA were respectively 29.1/7.1 (p<0.001). We found an almost perfect positive correlation between P1 rotation and sesamoid rotation, good correlation between IMA, MPA and SRA angles. There was poor correlation between pronation angles of the 1st phalanx and the 1st metatarsal. The 3D biometric algorithm combining IMA, MPA and SRA had a sensitivity of 95% and a specificity of 95.2% for the diagnosis of HV, compared to 90%/85.6% for the IMA and 90%/90.5% for the SRA.Conclusion:This original study confirmed our hypotheses. Weight Bearing CT efficiently analyzed the 3D architecture of the 1st ray in HV patients compared to asymptomatic controls. We concur with previous findings described in the literature concerning increased pronation of the 1st ray in HV. A novel biometric for HV using a specific multidimensional algorithm which combined IMA, HVA and SRA in a single 3D measurement, demonstrated increased sensitivity and specificity compared to the conventional 2D 1-2 intermetatarsal angle for the diagnosis of HV.

Multi-feature Multimodal Biometric Recognition Based on Quaternion Locality Preserving Projection

This paper proposed Quaternion locality preserving projection (QLPP) for multi-feature multimodal biometric recognition. Multi-features fill the real part or the three imaginary parts of quaternion to constitute the quaternion fusion features. In quaternion division ring, QLPP extracts the local information and finds essential manifold structure of the quaternion fusion features. Deferent from Quaternion principal component analysis (QPCA) and Quaternion fisher discriminant analysis (QFDA), QLPP takes advantage of the optimal linear approximations to find the nonlinear manifold structures. Two experiments are designed: one fuses four features from two biometric modalities, and the other fuses three features from three biometric modalities. The experimental results show the proposed algorithm achieves much better performance than the unimodal biometric algorithms, the traditional feature level fusion methods(weighted sum rule and series rule) and two quaternion representation methods(QPCA and QFDA).

Demographic Effects in Facial Recognition and Their Dependence on Image Acquisition: An Evaluation of Eleven Commercial Systems

We examined the effect of demographic factors on the performance of 11 commercial face biometric systems tested as part of the 2018 U.S. Department of Homeland Security, Science and Technology Directorate biometric technology rally. Each system that participated in this evaluation was tasked with acquiring face images from a diverse population of 363 subjects in a controlled environment. Biometric performance was assessed using a systematic, repeatable test process measuring both efficiency (transaction times), and accuracy (mated similarity scores) using a leading commercial algorithm. Prior works have documented differences in biometric algorithm performance across demographic categories and proposed that skin phenotypes offer a superior explanation for these differences. To test this concept, we developed an automatic method for measuring relative facial skin reflectance using subjects’ enrollment images and quantified the effect of this metric and other demographic covariates on performance using linear modeling. Both the efficiency and accuracy of the tested acquisition systems were significantly affected by multiple demographic covariates, including skin reflectance, gender, age, eyewear, and height. Skin reflectance had the strongest net linear effect on performance. Linear modeling showed that lower (darker) skin reflectance was associated with lower efficiency (higher transaction times) and accuracy (lower mated similarity scores). Skin reflectance was also a statistically better predictor of these effects than self-identified race labels. Unlike other covariates, the degree to which skin reflectance altered accuracy varied between systems. We show that the size of this skin reflectance effect was inversely related to the overall accuracy of the system such that the effect was almost negligible for the system with the highest overall accuracy. These results suggest that, in evaluations of biometric accuracy, the magnitude of measured demographic effects depends on image acquisition.

A multimodal authentication for biometric recognition system using hybrid fusion techniques

Biometric Recognition and authentication is used in every application for the secured identification of the persons. Several Researches has been carried out in the past decade that concerns only to enhance the accuracy of biometric algorithms for various kinds of features by ignoring the aspects of robustness and reliability. With this objective, new algorithm called effective linear scale authentication has been proposed which works on the principle of hybrid fusion of two feature inputs such as Hand geometry and iris of the users. The two different techniques have been adopted for the Feature Extraction. One is effective linear binary patterns and other is scale invariant fourier transform and it is stored in the databases for the further verification. This algorithm has been tested with the CASIA Image Datasets and the results had clearly proven that the novel idea of fusing the local features to address the scalar and angular inefficiencies of the existing methods.

РАЗРАБОТКА ОБЩЕЙ СТРУКТУРЫ АЛГОРИТМИЧЕСКОГО ОБЕСПЕЧЕНИЯ СБОРА И АНАЛИЗА ДАННЫХ ДЛЯ ОЦЕНКИ ЭМОЦИОНАЛЬНОЙ ЭКСПРЕССИИ ПРИ БИОМЕТРИЧЕСКОЙ АУТЕНТИФИКАЦИИ СУБЪЕКТОВ

Mimicry, emotions and emotional expression of a user of the information system deployed on the automated workplace are considered as biometric characteristics of the authentication system. Specific features of emotional expression of the alleged intruder, whose aim is violating information security through illegal infiltration into the information environment, have been determined. The methods and already implemented systems of automated identification of emotions according to facial expressions of the researched person have been described. It is proposed to modernize and to adapt these methods to solve the problems of information protection, namely, for use in the biometric system. It is suggested to consider the manifestation coefficient, duration of the moment of the onset of the facial muscles activity, duration of the moment of facial muscles activity culmination, and duration of the moment of disappearance of facial muscles activity of each basic emotion of a person (joy, surprise, sadness, fear, anger, contempt and disgust) as biometric characteristics. The developed algorithmic support consists of an algorithm that performs the determination of the minimum and maximum thresholds of each of the biometric characteristics and algorithm of the entire user authentication process and the architecture of the system to protect against unauthorized access to the information system. Also, the article gives an example of a user profile that is stored in a database, processed and improved by the system in the course of its operation.

Development of a Metrological Database with Images of a Human Face in the Infrared Range to Evaluate the Effectiveness of Biometric Algorithms

The work shows the promise of using biometric algorithms intended for remote determination of current human bio-parameters based on processing of the infrared image of his face. Problems that hinder the widespread use of remote biometric algorithms in practice are highlighted. The urgency of creating a metrological database containing video recordings of the human face image in the infrared range for the evaluation of the biometric algorithms efficiency is substantiated. Presented are the results of an analysis of existing databases containing images of a person’s face. Insufficient development of specialized databases containing infrared images of a person’s face was noted. The main shortcomings of such databases are highlighted. An approach to creating a metrological database based on the acquisition and storage of complex personal data is proposed. The main components of such data are video recordings of the face image in the visible and infrared ranges, time synchronized records of human bio-parameters, as well as information on the complexity of test tasks performed. The structure of the laboratory system for obtaining complex data on the person undergoing testing is considered. A typical data structure for a single test cycle is presented. The application results of the first version of the metrological database for experimental research of the effectiveness of algorithms for remote determination of human bio-parameters are presented.

Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT

A multimodal biometric system is applied to recognize individuals as authentication, identification and verification for claimed identity. Multimodal biometrics increases the security level accuracy, spoof of attacks, noise in collected data, intra-class variations, inter-class variations, non universality etc. In this paper a multi modal biometric algorithm is designed by integrating iris, palm print, face and signature based on encoded discrete wavelet transform for image analysis and authentication. Multi level wavelet based fusion approach is applied, integrated and encoded into single composite image for matching decision. It reduces the memory size, increases the recognition accuracy and ERR using multimodal biometric approach when compared to individual biometric traits. The complexity of fusion and the reconstruction algorithm is suitable for many real time applications.

Floating-point accelerator for biometric recognition on FPGA embedded systems

This paper aims at presenting a Floating-Point Biometric Accelerator (FPBA) specially designed to speed-up processing kernels used in biometric algorithms. The FPBA was developed in order to facilitate its inclusion as part of an embedded system that was implemented on a low-cost FPGA family from Xilinx. The advantage of this approach is that such algorithms can be programmed on the same hardware architecture following a software design flow. The internal design includes several blocks that compute basic operations and transcendental functions useful in biometrics. For comparison purposes, the execution time of four typical biometric kernels resolved by the FPBA were compared against the floating-point unit (FPU) provided by Xilinx. In all cases, the experimental results show that the FPBA reduces the execution time by a factor ranging from x7 to x22. The results also show the execution of two biometric recognition algorithms that are accelerated by x7 and x16.

Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology.

In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710±1.900% and 3.440±1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of-concept implementation is presented as an annex to this paper.

Evolution of Gait Biometric System and Algorithms- A Review

Evolution of Gait Biometric System and Algorithms- A Review

Use of synthetic data to test biometric algorithms

For digital imagery, face detection and identification are functions of great importance in wide-ranging applications, including full facial recognition systems. The development and evaluation of unique and existing face detection and face identification applications require a significant amount of data. Increased availability of such data volumes could benefit the formulation and advancement of many biometric algorithms. Here, the utility of using synthetically generated face data to evaluate facial biometry methodologies to a precision that would be unrealistic for a parametrically uncontrolled dataset, is demonstrated. Particular attention is given to similarity metrics, symmetry within and between recognition algorithms, discriminatory power and optimality of pan and/or tilt in reference images or libraries, susceptibilities to variations, identification confidence, meaningful identification mislabelings, sensitivity, specificity, and threshold values. The face identification results, in particular, could be generalized to address shortcomings in various applications and help to inform the design of future strategies.

Neural Network Based Normalized Fusion Approaches for Optimized Multimodal Biometric Authentication Algorithm

A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and face biometric traits. Normalized score level fusion approach is applied and optimized, encoded for matching decision. It is a multilevel wavelet, phase based fusion algorithm. This robust multimodal biometric algorithm increases the security level, accuracy, reduces memory size and equal error rate and eliminates unimodal biometric algorithm vulnerabilities.

Sudden Cardiac Death Detection Methods Based on ECG Biometric Technologies

Sudden Cardiac Death Detection Methods Based on ECG Biometric Technologies Sudden cardiac death (SCD), which occurs when there is an abrupt loss of heart function, has caused millions of deaths worldwide. The aim of our study was to detect SCD occurrence and provide SCD warnings by using ECG biometric algorithms. Two methods of detection were analysed, comprising modified zero-crossing and wavelet based methods. An average heartbeat was generated for each person by randomly selecting 20 normal sequential heartbeats to become their heartbeat template. For the modified zero-crossing method, when the SCD event correlation coefficients dropped substantially because of waveform dissimilarity, the threshold for a suspected SCD event was set at 0.7. After the correlation coefficients dropped below the threshold, an IIR filter with a center frequency of 5 Hz and the zero-crossing method were applied to further verify SCD events. Overall, the results showed that our algorithm successfully detected up to 98.48% accuracy. The wavelet method detected SCD events by using template similarities and wavelet coefficient plots. The wavelet analysis was applied to detect SCD with an overall performance of a 92.31% correct detection rate. Hence, this study demonstrated the possibility of developing an all-in-one system for monitoring life-threatening heartbeats while protecting personal medical data.

Towards a multi-source fusion approach for eye movement-driven recognition

This paper presents a research for the use of multi-source information fusion in the field of eye movement biometrics. In the current state-of-the-art, there are different techniques developed to extract the physical and the behavioral biometric characteristics of the eye movements. In this work, we explore the effects from the multi-source fusion of the heterogeneous information extracted by different biometric algorithms under the presence of diverse visual stimuli. We propose a two-stage fusion approach with the employment of stimulus-specific and algorithm-specific weights for fusing the information from different matchers based on their identification efficacy. The experimental evaluation performed on a large database of 320 subjects reveals a considerable improvement in biometric recognition accuracy, with minimal equal error rate (EER) of 5.8%, and best case Rank-1 identification rate (Rank-1 IR) of 88.6%. It should be also emphasized that although the concept of multi-stimulus fusion is currently evaluated specifically for the eye movement biometrics, it can be adopted by other biometric modalities too, in cases when an exogenous stimulus affects the extraction of the biometric features.

Semiautomated fault interpretation based on seismic attributes

Three-dimensional fault interpretation is a time-consuming and tedious task. Huge efforts have been invested in attempts to accelerate this procedure. We present a novel workflow to perform semiautomated fault illumination that uses a discontinuity attribute as input and provides labeled fault surfaces as output. The procedure is modeled after a biometric algorithm to recognize capillary vein patterns in human fingers. First, a coherence or discontinuity volume is converted to binary form indicating possible fault locations. This binary volume is then skeletonized to produce a suite of fault sticks. Finally, the fault sticks are grouped to construct fault surfaces using a classic triangulation method. The processing in the first two steps is applied time slice by time slice, thereby minimizing the influence of staircase artifacts seen in discontinuity volumes. We illustrate this technique by applying it to a seismic volume acquired over the Netherlands Sector of the North Sea Basin and find that the proposed strategy can produce highly precise fault surfaces.

A blind watermarking algorithm for fingerprint images based on contourlet transform

We present a novel blind biometric watermarking algorithm based on contourlet transform in this paper. The host image is decomposed into a series of multiscale, local and directional subimages using contourlet transform. A high frequency directional sub-band which has the highest energy is used for watermarking. At the same time, the original binary watermark is scrambled by two-dimensional Arnold transform, and then embedded into the selected directional sub-band. The signs of the contourlet coefficients are modified depending on the watermark bit. The watermark retrieving algorithm is a blind detection process, and it does not need the original image. Iris code is taken as the watermark and finger print image is taken as the host image. The simulation results show that the proposed watermarking algorithm gives good perceptual transparency and is able to resist additive channel noise attacks, cropping, histogram equalisation, gamma correction, etc.