On Efficiency and Effectiveness Tradeoffs in High-Throughput Facial Biometric Recognition Systems

The 2018 Biometric Technology Rally was an evaluation, sponsored by the U.S. Department of Homeland Security, Science and Technology Directorate (DHS S&T), that challenged industry to provide face or face/iris systems capable of unmanned, traveler identification in a high-throughput security environment. Selected systems were installed at the Maryland Test Facility (MdTF), a DHS S&T affiliated bio-metrics testing laboratory, and evaluated using a population of 363 naive human subjects recruited from the general public. The performance of each system was examined based on measured throughput, capture capability, matching capability, and user satisfaction metrics. This research documents the performance of unmanned face and face/iris systems required to maintain an average total subject interaction time of less than 10 seconds. The results highlight discrepancies between the performance of biometric systems as anticipated by the system designers and the measured performance, indicating an incomplete understanding of the main determinants of system performance. Our research shows that failure-to-acquire errors, unpredicted by system designers, were the main driver of non-identification rates instead of failure-to-match errors, which were better predicted. This outcome indicates the need for a renewed focus on reducing the failure-to-acquire rate in high-throughput, unmanned biometric systems.

There is insufficient consideration of Generation Z’s cultural and generational needs in the implementation of biometric attendance systems in Arabic educational settings. This study delves into Generation Z’s discipline, exploring their perspectives on attendance systems and aligning commitment with their interests. The primary aim is to gauge biometric systems’ impact on productivity. Google Form questionnaires collected data from young employees, ages 25 to 35, who belong to Generation Z’s working in the higher education system. Structural equation modeling and descriptive analysis assessed the data. While biometric systems enhance discipline, they may dampen morale. Implementing systems fairly and maintaining flexibility is vital. The study underscores the importance of evaluating employees based on achievements. It sheds light on biometric systems’ role in attendance management and organizational performance, aiding HR practices. The results showed no significant effect of Employee Management Practices (EMP) on organization performance through Biometric Attendance Technology (BAT) (B = 0.049, t = 1.330, p = 0.184). Nor significant effects of Organizational Performance Metrics (OPM) (B = 0.019, t = 0.608, p = 0.543). Technological Infrastructure (TI) (B = 0.019, t = 0.2461, p = 0.645), or Satisfaction and Engagement (ESE) (B = 0.057, t = 1.381, p = 0.167) on organization performance through Biometric Attendance Technology. The mediator impact was also found to be not significant (P > 0.05). Therefore, both direct and specific indirect effects were not significant. Indicating that Biometric Attendance Technology does not mediate the relationship between these variables and organizational performance.

With the use of computers and Internet in every major activity of our society, security is increasingly important. Biometric recognition is not only challenging but also computationally demanding. This chapter aims develop an iris biometric system. The iris has the advantages of uniqueness, stableness, anti-spoof, non-invasiveness and efficiency and could be applied in almost every area (banking, forensics, access control, etc.). The performance of a biometric classification system is largely depending on the techniques used for feature extraction. Inspired by the biological plausibility of ordinal measures, we propose their employment for iris representation and recognition. Qualitative measurement, associated to the relative ordering of different characteristics, is defined as ordinal measurement. Besides the proposing of a novel, fast and robust, ordinal based feature extraction method, the chapter also considers the problem of designing the decision making model so as to obtain an efficient and effective biometric system. In the literature, there are different approaches for iris recognition, nevertheless, there are still challenging open problems in improving the accuracy, robustness, security and ergonomics of biometric systems.

Score normalization applied to adaptive biometric systems

The Port Authority of New York and New Jersey (PANYNJ) is an interstate authority that builds, owns, and operates a region-wide multi-modal transportation infrastructure which includes tunnels, bridges, airports, seaports, bus stations, and the World Trade Center. As custodian of infrastructure that is critical to the regional and national economy and the national logistics supply chain, the Authority faces difficult choices in allocating State of Good Repair (SGR) resources to its many assets, some of which are a century old. Furthermore, SGR imperatives often compete with emerging hazards (e.g, security, flooding) for scarce resources, creating a dichotomy of choice. These circumstances led the PANYNJ to investigate a process that would evaluate project priority on the basis of multi-hazard resilience metrics. Tunnels, Bridges &Terminal (TB&T) Department of the PANYNJ is currently investigating the development of a resilience management framework for critical agency infrastructure that focuses on resilience to all disruptions, incorporates lessons learned, and rationalizes upgrades across a broad spectrum of hazards. Building on the risk and resilience methodologies introduced by the Department of Homeland Security, Science and Technology Directorate (DHS-S&T), the PANYNJ just completed a pilot program in which these existing resilience management methodologies were customized to vehicular tunnels and bus terminals. The pilot effort applied the resilience management methodologies to the Holland Tunnel and the Port Authority Bus Terminal to evaluate their resilience to Superstorm Sandy-like flood hazards. This paper will summarize some of the key contributions in this pilot effort: the development and demonstration of a resilience-based framework for prioritizing projects. The large range in costs and benefits of the piloted projects highlighted the need to quantify the far-reaching downstream consequences of a project’s improvements, as well as the ability to capture sensitivity of hypercritical assets. Project performance metrics were developed that encompass measures of enhancement to resilience, the cascading effects and their extents, to provide a more comprehensive means of capturing project benefits.

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.

Automated Systems.

This chapter will present a detailed overview of biometric systems and how they work. The system attributes, performance metrics, modalities are all discussed, alongside an analysis of attacks that exist against biometric systems. Given the limitations that exist in all biometric systems, a trend in biometric research is towards the use of multibiometric systems, which seek to overcome the issues through the application of multiple modalities, algorithms, instances or samples. These systems and the approaches that exist are presented. The chapter ends with a discussion of the efforts being made with the standardization of the domain - which has been the essential factor in bringing biometrics from a niche into the mainstream.

As biometric systems are deployed in increasingly diverse applications, it becomes correspondingly important to understand the impact which human aging has on system performance. Aging directly affects those physiological and behavioral traits which are characterized in biometric measurements, and a practical biometric system must be designed to account for age-induced changes. However, age can also have very positive implications, for example as a source of further identification information. This paper reviews research to understand how age factors impinge on biometric systems and uses this to synthesize a system infrastructure to unify implementation principles. We present new results to show how multiagent structures can provide an effective framework for this purpose, enhancing performance in both identification and predictive scenarios.

The use of biometric systems in physical access scenarios is gaining popularity. In such scenarios, users are enroled under well controlled conditions and the enrolment is usually indoors. To gain access to the building, the user provides his biometric samples in an outdoor environment over which there is little control. This adversely affects the quality of the samples and as a result the system performance is sub-optimal. This study evaluates the performance of a multimodal biometric system in a physical access scenario. We evaluate leading commercial algorithms on an indoor-outdoor, multimodal database comprising of face and voice samples. The indoor-outdoor nature of the database and the choice of modalities results in individual systems performing poorly. Popular normalization and fusion techniques are used to improve the performance of the overall system. Multimodal fusion results in an average improvement of approximately 20% at 1% false acceptance rate over individual modalities.

We address the problem of performance evaluation in biometric verification systems. By formulating the optimum Bayesian decision criterion for a verification system and by assuming the data distributions to be multinormals, we derive two statistical expressions for calculating theoretically the false acceptance and false rejection rates. Generally, the adoption of a Bayesian parametric model does not allow for obtaining explicit expressions for the calculation of the system errors. As far as biometric verification systems are concerned, some hypotheses can be reasonably adopted, thus allowing simple and affordable expressions to be derived. By using two verification system prototypes. Based on hand shape and human face, respectively, we show our results are well founded.

The human iris is one of the most reliable biometric features. Since it is a live organ, variations caused by pupil contraction/dilation will degrade the performance of a biometric system based on iris. This paper presents a method for generating images of irises with a specific dilation coefficient. The approach presented here uses a mathematical model that aims to emulate the dynamic of the iris with respect to the pupil dilation/contraction. The estimated images are generated using image an image warping technique. The iris image is approximated by re-mapping the radius of the polar coordinates using a nonlinear function. The proposed method benefits of low complexity and provides better results with respect to the photorealistic aspect and to the performance of iris biometric systems. The performance of the proposed model applied to the iris as a biometric system is tested using a commercial iris recognition system.

Unimodal biometric systems often face significant limitations due to sensitivity to noise intraclass variability and other factors. Multimodal biometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. However, the multimodal biometric system is limited to the time constraints due to its multiple processing stages. To overcome these limitations this paper presents a fast multimodal verification system by using the dynamic regions of the fingerprint image and enhanced iris segmentation method. The multimodal system is fused using rank level fusion at the verification stage. The performance of the biometric system shows improvement in the False Acceptance Rate (FAR) and Equal Error Rate (EER) curves. Furthermore, the time taken for the training and verification phase has a reduction of 15% when compared with the existing system tested on the slow processing mobile devices.

Various biometric traits are available but recently finger knuckle image has attracted great attention to biometric research community due to its potentiality and ease of use. The performance of any biometric system heavily depends on the accuracy of feature detection and robustness of feature description. A number of feature descriptors are available but selection is being determined by the type of application such as image retrieval, biometric system, remote sensing etc. This paper proposed a biometric system using leading descriptor for finger knuckle biological trait image recognition and also compare the proposed system with existing leading state-of-art finger knuckle print recognition. The recognition performance is measured by some standard evaluation protocol such as Equal Error Rate (EER), Decidability index, Computation cost, Zero-score imposter probability, Zero-score genuine probability, Receiver Operating Characteristic (ROC), Detection Error Trade-off (DET) over PolyU Finger Knuckle benchmark database. The experimental results show that, the performance of SURF, KAZE and ORB are comparable and are better as compared to BRISK and MSER descriptor. The ERR of 0.0010% is obtained with ORB descriptor while the Decidability index of 6.4645 is obtained for KAZE. The minimum Computational cost of 0.1442 s is obtained for SURF as compares to other of its class.

A novel compression-based 2D-chaotic sine map for enhancing privacy and security of biometric identification systems