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Journal articles on the topic 'Biometric software'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Alam*, Varisha, and Dr Mohammad Arif. "Classification of Large Biometric Data in Database System." International Journal of Innovative Technology and Exploring Engineering 10, no. 10 (August 30, 2021): 1–8. http://dx.doi.org/10.35940/ijitee.d8592.08101021.

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"Biometrics" is got from the Greek word 'life' and 'measure' which implies living and evaluation take apart. It simply converts into "life estimation". Biometrics uses computerized acknowledgment of people, dependent on their social and natural attributes. Biometric character are data separated from biometric tests, which can use for examination with a biometric orientation. Biometrics involves techniques to unusually recognize people dependent on at least one inherent physical or behavior attribute. In software engineering, specifically, biometric is used as a form of character retrieve the Committee and retrieve command. Biometric identically utilized to recognize people in bunches that are in observation. Biometric has quickly risen like a auspicious innovation for validation and has effectively discovered a spot in most of the scientific safety regions. An effective bunching method suggest for dividing enormous biometrics data set through recognizable proof. This method depends on the changed B+ tree is decreasing the discs get to. It diminishes the information recovery time and also possible error rates. Hence, for bigger applications, the need to reduce the data set to a more adequate portion emerges to accomplish both higher paces and further developed precision. The main motivation behind ordering is to recover a small data set for looking through the inquiry

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NAZAR, AKIF, ISSA TRAORÉ, and AHMED AWAD E. AHMED. "INVERSE BIOMETRICS FOR MOUSE DYNAMICS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (May 2008): 461–95. http://dx.doi.org/10.1142/s0218001408006363.

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Various techniques have been proposed in different literature to analyze biometric samples collected from individuals. However, not a lot of attention has been paid to the inverse problem, which consists of synthesizing artificial biometric samples that can be used for testing existing biometric systems or protecting them against forgeries. In this paper, we present a framework for mouse dynamics biometrics synthesis. Mouse dynamics biometric is a behavioral biometric technology, which allows user recognition based on the actions received from the mouse input device while interacting with a graphical user interface. The proposed inverse biometric model learns from random raw samples collected from real users and then creates synthetic mouse actions for fake users. The generated mouse actions have unique behavioral properties separate from the real mouse actions. This is shown through various comparisons of behavioral metrics as well as a Kolmogorov–Smirnov test. We also show through a two-fold cross-validation test that by submitting sample synthetic data to an existing mouse biometrics analysis model we achieve comparable performance results as when the model is applied to real mouse data.

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Maiorana, Emanuele, Patrizio Campisi, and Alessandro Neri. "Template Protection and Renewability for Dynamic Time Warping Based Biometric Signature Verification." International Journal of Digital Crime and Forensics 1, no. 4 (October 2009): 40–57. http://dx.doi.org/10.4018/jdcf.2009062404.

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In this article, the authors propose a protected on-line signature based biometric authentication system, where the original signature templates are protected by transforming them in a non-invertible way. Recovering the original biometrics from the stored data is thus computationally as hard as random guessing them. The transformed templates are compared employing a Dynamic Time Warping (DTW) matching strategy. The reported experimental results, evaluated on the public MCYT signature database, show that the achievable recognition rates are only slightly affected by the proposed protection scheme, which is able to guarantee the desired security and renewability for the considered biometrics.

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LI, Peng, Jie TIAN, Xin YANG, Peng SHI, and Yang-Yang ZHANG. "Biometric Template Protection." Journal of Software 20, no. 6 (July 14, 2009): 1553–73. http://dx.doi.org/10.3724/sp.j.1001.2009.03528.

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Bayly, Duncan, Maurice Castro, Arathi Arakala, Jason Jeffers, and Kathy Horadam. "Fractional biometrics: safeguarding privacy in biometric applications." International Journal of Information Security 9, no. 1 (December 1, 2009): 69–82. http://dx.doi.org/10.1007/s10207-009-0096-z.

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6

Peer, P., Ž. Emeršič, J. Bule, J. Žganec-Gros, and V. Štruc. "Strategies for Exploiting Independent Cloud Implementations of Biometric Experts in Multibiometric Scenarios." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/585139.

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Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper.

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Ming, Anlong, and Huadong Ma. "The Biometrics Grid: A Solution to Biometric Technologies." IEEE Distributed Systems Online 8, no. 9 (September 2007): 1. http://dx.doi.org/10.1109/mdso.2007.4370097.

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Gu, Datong, Minh Nguyen, and Weiqi Yan. "Cross Models for Twin Recognition." International Journal of Digital Crime and Forensics 8, no. 4 (October 2016): 26–36. http://dx.doi.org/10.4018/ijdcf.2016100103.

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Nowadays, Biometrics has become a popular tool in personal identification as it can use physiological or behavioral characteristics to identify individuals. Recent advances in information technology has increased the accuracy of biometric to another level, there is still a slew of problems existed, such as complex environment, aging and unique problems. Among many classes of identifications, recognizing twins is one of the most difficult tasks as they resemble each other. This affects the use of biometrics in general cases and raises potential risks of biometrics in access control. In this paper, the authors manage to distinguish twins using four different models, namely, face recognition, ear recognition, voice recognition and lip movement recognition. Their results show that voice recognition has the best performance in twin recognition with 100% accuracy. This is much higher than that of face recognition and ear recognition (with 58% and 53% respectively); and lip movement recognition that yields 76% accuracy.

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KHUWAJA, GULZAR A., and A. N. ABU-REZQ. "DATA ACQUISITION AND RECOGNITION OF FINGERPRINTS WITH LVQ." International Journal of Computational Intelligence and Applications 03, no. 01 (March 2003): 65–88. http://dx.doi.org/10.1142/s1469026803000811.

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A typical biometric system has three distinct phases. These are biometric data acquisition, feature extraction, and decision-making. The first step, the acquisition phase, is extremely important. If high quality images are not obtained, the next phase cannot operate reliably. Fingerprint recognition remains as one of the most prominent biometric identification methods. In this paper, we develop a prototype optical-based fingerprints data acquisition system using a CCD digital still camera to capture a complete impression of finger area required for accurately identifying an individual and present an image-based approach for online fingerprint recognition with the objective to increase the overall matching performance. The fingerprint images are matched based on features extracted with an adaptive learning vector quantization (LVQ) neural network to yield peak recognition of 98.6% for a random set of 300 test prints (100 fingers × 3 images). This system can be adopted as a multi-modal biometrics where two or more fingers are matched.

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Milinković, Milorad, Miroslav Minović, and Miloš Milovanović. "Applying Technical Standards to Biometric Systems." International Journal of Decision Support System Technology 8, no. 2 (April 2016): 50–59. http://dx.doi.org/10.4018/ijdsst.2016040104.

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Nowadays, the development and the application of biometric systems on one hand, and the large number of hardware and software manufacturers on the other, caused two the most common problems of biometric systems: a problem of interoperability between system's components as well as between different biometric systems and a problem of biometric data security and privacy protection, both in storage and exchange. Specifications and standards, such as BioAPI and CBEFF, registered and published as multiple standards by ISO (International Organization for Standardization), propose the establishment of single platform (BioAPI) to facilitate the functioning of the biometric systems regardless of hardware or software manufacturers, and unique format for data exchange (CBEFF) to secure biometric data. In this paper, these standards are analyzed in detail and considered as possible solutions to aforementioned problems.

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Raj, Sunny, Jodh S. Pannu, Steven L. Fernandes, Arvind Ramanathan, Laura L. Pullum, and Sumit K. Jha. "Attacking NIST biometric image software using nonlinear optimization." Pattern Recognition Letters 131 (March 2020): 79–84. http://dx.doi.org/10.1016/j.patrec.2019.12.003.

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Chandra, Akhilesh, and Thomas G. Calderon. "Toward a Biometric Security Layer in Accounting Systems." Journal of Information Systems 17, no. 2 (September 1, 2003): 51–70. http://dx.doi.org/10.2308/jis.2003.17.2.51.

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This paper discusses theoretical and practical issues related to the use of a biometric-enabled security layer in accounting systems aimed at enhancing user authentication and reducing control risk. Originating in criminology, biometric technology has matured over the years with applications in diverse disciplines. However, its use in business and accounting is still in its infancy, and many issues about its role in information systems security are unresolved. The paper proposes an access decision framework that draws from the strategy and risk assessment literature to model processes where biometrics might be used to reduce control risk. Despite its potential strengths, biometric technology is not a panacea and represents one element in a portfolio of security mechanisms needed to protect information resources. The paper discusses challenges in implementing biometric technology and identifies avenues for future research.

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De Felice, Elena, Cesare Pacioni, Federico Maria Tardella, Cecilia Dall’Aglio, Antonio Palladino, and Paola Scocco. "A Novel Method for Increasing the Numerousness of Biometrical Parameters Useful for Wildlife Management: Roe Deer Mandible as Bone Model." Animals 10, no. 3 (March 11, 2020): 465. http://dx.doi.org/10.3390/ani10030465.

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Study of dimensions (biometry) and shapes (geometric morphometry) of bone structures in ungulates is of extreme importance in wildlife population management. Unlike classical biometry, which involves the use of a caliper for measurements, geometric morphometry acquires, through software, a series of reference points (landmarks) from digital photos, providing a series of linear measures. A method to convert values obtained from the GeoGebra software into biometric measures is described. We took photos of 25 mandibles of adult roe deer and at the same time measured mandible length and teeth row length using a caliper. After image processing using GeoGebra, we calculated the conversion factor as the mean ratio between measures taken using GeoGebra and the caliper. The series of measurements, taken with two different methods (direct measurement using the caliper and conversion from GeoGebra output), showed a good degree of agreement. We used the conversion factor to obtain, from the GeoGebra database, four additional parameters of 50 mandibles. The analysis of variance showed that one parameter was significantly different between sexes (p = 0.04), demonstrating the usefulness of the measurement conversion. The conversion factor is helpful to improve classical biometric databases to better clarify the relationship between environment and wildlife status.

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Kotzerke, Johannes, Stephen A. Davis, Jodie McVernon, and Kathy J. Horadam. "Steps to solving the infant biometric problem with ridge‐based biometrics." IET Biometrics 7, no. 6 (April 18, 2018): 567–72. http://dx.doi.org/10.1049/iet-bmt.2017.0282.

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Thanki, Rohit, Surekha Borra, and Ashish Kothari. "Fragile Watermarking Framework for Tamper Detection of Color Biometric Images." International Journal of Digital Crime and Forensics 13, no. 2 (March 2021): 35–56. http://dx.doi.org/10.4018/ijdcf.2021030103.

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Application of fragile watermarking on biometric images stored at a server or cloud ensures proper authentication and tamper detection when access to the servers was shared. In this paper, a hybrid domain fragile watermarking technique for authenticity of color biometric images, using hybridization of various transforms such as discrete cosine transform (DCT), fast discrete curvelet transform (FDCuT), and singular value decomposition (SVD) is proposed. The hybrid transform coefficients are modified according to the scrambled color watermark to obtain watermarked color biometric image. The security of this technique is strengthened with the usage of Arnold scrambling, and by using multiple secret keys. The proposed technique is analyzed on FEI Brazilian face database. The experimental results show that this technique performs better than the existing fragile watermarking techniques.

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Paul, Padma P., and Marina L. Gavrilova. "Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (July 2013): 80–94. http://dx.doi.org/10.4018/ijcini.2013070105.

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Biometric fusion to achieve multimodality has emerged as a highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. The authors have proposed new type of biometric fusion called cancelable fusion. The idea behind the cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that the stored biometric template can be easily changed in a biometric security system. Cancelable fusion does the fusion of multiple biometric trait in addition it preserve the properties of cancelability. In this paper, the authors present a novel architecture for template generation within the context of the cancelable multibiometric fusion. The authors develop a novel cancelable biometric template generation algorithm using cancelable fusion, random projection and transformation-based feature extraction and selection. The authors further validate the performance of the proposed algorithm on a virtual multimodal face and ear database.

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Trabelsi, Zouheir, Mohamed Al Hemairy, and Mohammad M. Masud. "On Investigating the Effectiveness of Biometric Readers in Thwarting Network Attacks: A Secure Architecture Design Proposal." Journal of Intelligent Systems 24, no. 2 (June 1, 2015): 199–213. http://dx.doi.org/10.1515/jisys-2014-0049.

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AbstractBiometrics readers are deployed in many public sites and are used for user identification and verification. Nowadays, most biometrics readers can be connected to local area networks, and consequently, they are potential targets for network attacks. This article investigates the robustness of several fingerprint and iris readers against common denial of service (DoS) attacks. This investigation has been conducted using a set of laboratory experiments and DoS attack generator tools. The experiments show clearly that the tested biometric readers are very vulnerable to common DoS attacks, and their recognition performances deteriorate significantly once they are under DoS attacks. Finally, the article lists some security consideration that should be taken into consideration when designing secure biometrics readers.

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Hoo, Seng Chun, and Haidi Ibrahim. "Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements." Journal of Sensors 2019 (September 15, 2019): 1–25. http://dx.doi.org/10.1155/2019/7410478.

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The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

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Conti, Vincenzo, Carmelo Militello, Filippo Sorbello, and Salvatore Vitabile. "Biometric sensors rapid prototyping on field-programmable gate arrays." Knowledge Engineering Review 30, no. 2 (March 2015): 201–19. http://dx.doi.org/10.1017/s0269888914000307.

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AbstractBiometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the developers to design and prototype the whole embedded biometric sensors. In this work, two strong and invasive biometric traits, such as fingerprint and iris, have been considered, analyzed, and combined in unimodal and multimodal biometric sensors. Biometric sensor performance has been evaluated using the well-known FVC2002, CASIA, and BATH databases.

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Shao, Huikai, Dexing Zhong, and Xuefeng Du. "A deep biometric hash learning framework for three advanced hand‐based biometrics." IET Biometrics 10, no. 3 (March 16, 2021): 246–59. http://dx.doi.org/10.1049/bme2.12014.

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Stanton, Brian, Mary Theofanos, Shahram Orandi, Ross Micheals, and Nien-Fan Zhang. "Effects of Scanner Height on Fingerprint Capture." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 10 (October 2007): 592–96. http://dx.doi.org/10.1177/154193120705101005.

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Although the deployment of biometric technologies such as fingerprints is becoming more widespread, little attention is being paid to the human-computer interaction that such technologies involve. Most biometric systems employ both hardware and software measures to maximize the capture quality of the biometric data. The physical presentation of the biometric data by the participant to the system involves many anthropometric and ergonomic factors that have been largely ignored. This study examined the effect of the height of the sensor on the quality and the time required to collect fingerprints. User performance, both fingerprint quality and timing, was impacted by scanner height.

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Rudall, B. H. "Biometric systems." Kybernetes 33, no. 8 (September 2004): 1235–43. http://dx.doi.org/10.1108/03684920410545225.

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Fàbregas, Joan, and Marcos Faundez-Zanuy. "Biometric dispersion matcher." Pattern Recognition 41, no. 11 (November 2008): 3412–26. http://dx.doi.org/10.1016/j.patcog.2008.04.020.

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Fairhurst, Michael, Cheng Li, and Márjory Da Costa‐Abreu. "Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data." IET Biometrics 6, no. 6 (July 13, 2017): 369–78. http://dx.doi.org/10.1049/iet-bmt.2016.0169.

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Kaur, Harkeerat, and Pritee Khanna. "Privacy preserving remote multi-server biometric authentication using cancelable biometrics and secret sharing." Future Generation Computer Systems 102 (January 2020): 30–41. http://dx.doi.org/10.1016/j.future.2019.07.023.

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Etter, Lauren, Alinani Simukanga, Wenda Qin, Rachel Pieciak, Lawrence Mwananyanda, Margrit Betke, Jackson Phiri, Caroline Carbo, Arnold Hamapa, and Chris Gill. "Project SEARCH (Scanning EARs for Child Health): validating an ear biometric tool for patient identification in Zambia." Gates Open Research 4 (November 6, 2020): 168. http://dx.doi.org/10.12688/gatesopenres.13197.1.

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Patient identification in low- to middle-income countries is one of the most pressing public health challenges of our day. Given the ubiquity of mobile phones, their use for health-care coupled with a biometric identification method, present a unique opportunity to address this challenge. Our research proposes an Android-based solution of an ear biometric tool for reliable identification. Unlike many popular biometric approaches (e.g., fingerprints, irises, facial recognition), ears are noninvasive and easily accessible on individuals across a lifespan. Our ear biometric tool uses a combination of hardware and software to identify a person using an image of their ear. The hardware supports an image capturing process that reduces undesired variability. The software uses a pattern recognition algorithm to transform an image of the ear into a unique identifier. We created three cross-sectional datasets of ear images, each increasing in complexity, with the final dataset representing our target use-case population of Zambian infants (N=224, aged 6days-6months). Using these datasets, we conducted a series of validation experiments, which informed iterative improvements to the system. Results of the improved system, which yielded high recognition rates across the three datasets, demonstrate the feasibility of an Android ear biometric tool as a solution to the persisting patient identification challenge.

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Lai, Kenneth, Ondřej Kanich, Michal Dvořák, Martin Drahanský, Svetlana Yanushkevich, and Vlad Shmerko. "Biometric‐enabled watchlists technology." IET Biometrics 7, no. 2 (August 30, 2017): 163–72. http://dx.doi.org/10.1049/iet-bmt.2017.0036.

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Chinwe C, Ibebuogu, Philip Seth, and Anyaduba Obiageli J. "Biometric Authentication System Using Face Geometry." International Journal of Engineering and Computer Science 8, no. 08 (August 20, 2019): 24805–13. http://dx.doi.org/10.18535/ijecs/v8i08.4332.

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This seminar paper deals with biometric authentication, using human facial geometry as the login verification parameter. Biometric is the measurement and statistical analysis of human's unique physical and behavioral characteristics by mapping face geometry, fingerprints, iris, and voice. The aim of this seminar paper is to develop a functional Biometric Authentication System Using Face Geometry as the authentication method. Facial Geometry Authentication is a category of biometric technology that maps an individual's facial features mathematically and stores the data as face-prints in a database. Meanwhile the objective of this paper is to improve data access security, enhance identification accuracy, and contribute to the improvement of the existing facial recognition systems, focusing mainly on increasing its accuracy performance. On the other hand, hacking into users’ privacy, loss of confidential information, and high running costs, among others geared the motivation to develop a biometric authentication system using face geometry. Furthermore, the software engineering methodology adopted in this seminar paper is the Structural System Analysis and Design Methodology (SSADM). The software is developed in Visual C-sharp, and the database in SQL Server 2012; using Microsoft Visual Studio 2012 as the integrated development environment. Besides, OpenCV 2.4.8 library is used for image processing, image mapping, and computer vision. The expected result of this seminar paper include: improved user data security, and increased efficiency in facial detection/recognition.

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Badmus, E. O., O. P. Odekunle, and D. O. Oyewobi. "Smart Fingerprint Biometric and RFID Time-Based Attendance Management System." European Journal of Electrical Engineering and Computer Science 5, no. 4 (July 27, 2021): 34–39. http://dx.doi.org/10.24018/ejece.2021.5.4.339.

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Attendance documentation is a crucial metric for determining a student's dedication, qualification, and status. Several types of systems have been developed for automated attendance. The majority of these implementations were built on a single factor template, representing a line of security error. In recent years, rapid development has taken place in several applications such as authorization, data access, and access control by implementing biometrics and radio frequency identification technologies. This research work focuses on designing a smart protocol for RFID authentication in combination with fingerprint biometric information, which guarantees confidentiality, identity verification, and data protection. Reaction time and event performance evaluation were the tests performed on the system. Ten students with unique fingerprints and RFID cards are assessed for the test. The test results show an average duration of 20.61 respective seconds for each student, and zero percent of false refusal rate was recorded in the implementation. Finally, the result shows the system is reliable and efficient. Each result revealed high-speed performance due to software and hardware coordination. Biometric systems provided data integrity and security, RFID provided limited complexity, and the proposed method's technical programming pattern provided an average run time.

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Gao, Guangwei, Jian Yang, Songsong Wu, Xiaoyuan Jing, and Dong Yue. "Bayesian sample steered discriminative regression for biometric image classification." Applied Soft Computing 37 (December 2015): 48–59. http://dx.doi.org/10.1016/j.asoc.2015.07.034.

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Hom Choudhury, Surabhi, Amioy Kumar, and Shahedul Haque Laskar. "Biometric Authentication through Unification of Finger Dorsal Biometric Traits." Information Sciences 497 (September 2019): 202–18. http://dx.doi.org/10.1016/j.ins.2019.05.045.

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Anil K. Jain and U. Uludag. "Hiding biometric data." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 11 (November 2003): 1494–98. http://dx.doi.org/10.1109/tpami.2003.1240122.

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Moline, Danae Bacells. "My face is yours: facial recognition software and copyright ownership." Interactive Entertainment Law Review 3, no. 2 (December 23, 2020): 138–46. http://dx.doi.org/10.4337/ielr.2020.02.06.

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The popular NBA 2K series include a facial recognition software that scans the user's face to generate a lookalike avatar. However, end user licence agreements provide for all intellectual property rights, including copyright, to be licensed or assigned to game publishers or developers. Consequently, the user may have no say whether an avatar with their facial features may be used, for instance in advertising for the game. In addition, the facial features stored in the game may be biometric data, and thus subject to strict data protection rules. This paper will analyse whether the avatar generated using a face scan is a copyrightable work of authorship. The analysis questions whether the face scan fits into different categories of works; photography, film and databases, including the sui generis photography and database rights. It concludes that copyright fails to protect the individual's facial features. Even so, the ownership clause in licence agreements on the one hand and the facial features as biometric data on the other further complicate the question of what the individual can assert as his own.

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Moline, Danae Bacells. "My face is yours: facial recognition software and copyright ownership." Interactive Entertainment Law Review 3, no. 2 (December 23, 2020): 138–46. http://dx.doi.org/10.4337/ielr.2020.02.06.

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The popular NBA 2K series include a facial recognition software that scans the user's face to generate a lookalike avatar. However, end user licence agreements provide for all intellectual property rights, including copyright, to be licensed or assigned to game publishers or developers. Consequently, the user may have no say whether an avatar with their facial features may be used, for instance in advertising for the game. In addition, the facial features stored in the game may be biometric data, and thus subject to strict data protection rules. This paper will analyse whether the avatar generated using a face scan is a copyrightable work of authorship. The analysis questions whether the face scan fits into different categories of works; photography, film and databases, including the sui generis photography and database rights. It concludes that copyright fails to protect the individual's facial features. Even so, the ownership clause in licence agreements on the one hand and the facial features as biometric data on the other further complicate the question of what the individual can assert as his own.

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Gomez‐Barrero, Marta, Christian Rathgeb, Ulrich Scherhag, and Christoph Busch. "Predicting the vulnerability of biometric systems to attacks based on morphed biometric information." IET Biometrics 7, no. 4 (March 12, 2018): 333–41. http://dx.doi.org/10.1049/iet-bmt.2017.0144.

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Tilton, Catherine J. "Biometric Standards — An Overview." Information Security Technical Report 7, no. 4 (December 2002): 36–48. http://dx.doi.org/10.1016/s1363-4127(02)00405-3.

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Furnell, Steven, and Nathan Clarke. "Inhabiting the Biometric Society." ITNOW 60, no. 3 (2018): 42–43. http://dx.doi.org/10.1093/itnow/bwy074.

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RADHIKA, K. R., S. V. SHEELA, M. K. VENKATESHA, and G. N. SEKHAR. "SIGNATURE AND IRIS AUTHENTICATION BASED ON DERIVED KINEMATIC VALUES." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (December 2010): 1237–60. http://dx.doi.org/10.1142/s021800141000841x.

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Authentication systems which are covenant with a measurable behavioral and physiological traits are essential for an online system. In this paper, two types of biometric sample authentication from different databases on a common algorithm using Continuous Dynamic Programming [CDP] are discussed. Using a common algorithm, a method for user-dependent threshold decisions can be achieved for both biometrics in a uniform fashion. The integration of static iris information and dynamic signature information are done at decision level. Inferences are drawn using voting techniques. The derived kinematic feature, acceleration, is used in this paper.

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Wing, Brad. "NIST Contributions to Biometric Technology." IT Professional 16, no. 2 (March 2014): 38–44. http://dx.doi.org/10.1109/mitp.2013.89.

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O., M., and David Sanka. "Implementation of Advance Encryption Standard (AES) in Biometric Electronic Voting Software." Communications on Applied Electronics 4, no. 3 (January 26, 2016): 30–42. http://dx.doi.org/10.5120/cae2016652052.

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Liu-Jimenez, J., R. Sanchez-Reillo, L. Mengibar-Pozo, and O. Miguel-Hurtado. "Optimisation of biometric ID tokens by using hardware/software co-design." IET Biometrics 1, no. 3 (September 1, 2012): 168–77. http://dx.doi.org/10.1049/iet-bmt.2012.0004.

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de Luis-Garcı́a, Rodrigo, Carlos Alberola-López, Otman Aghzout, and Juan Ruiz-Alzola. "Biometric identification systems." Signal Processing 83, no. 12 (December 2003): 2539–57. http://dx.doi.org/10.1016/j.sigpro.2003.08.001.

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Zhifang Wang, Shuangshuang Wang, and Qun Ding. "Security of Multimodal Biometric Fusion System." International Journal of Digital Content Technology and its Applications 5, no. 4 (April 30, 2011): 264–70. http://dx.doi.org/10.4156/jdcta.vol5.issue4.32.

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QI, MIAO, JUN KONG, YINGHUA LU, NING DU, and ZHIQIANG MA. "CONTENT-BASED BIOMETRIC IMAGE HIDING APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (June 2011): 565–81. http://dx.doi.org/10.1142/s0218001411008701.

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Recently, the use of information hiding techniques to protect biometric data has been an active topic. This paper proposes a novel image hiding approach based on correlation analysis to protect network-based transmitted biometric image for identification. Firstly, the correlation between the biometric image and the cover image is analyzed using principal component analysis (PCA) and genetic algorithm (GA). The purpose of correlation analysis is to enable the cover image to represent the secret image in content as much as possible, not just as a carrier of hidden information. Then, the unrepresented part of the biometric image, as the secret image, is encrypted and hidden into the middle-significant-bit plane (MSB) of the cover image redundantly. Extensive experimental results demonstrate that the proposed hiding approach not only gains good imperceptibility, but also resists some common attacks validated by the biometric identification accuracy.

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Kuznetsov, A. A. "Legal Basis of Biometric Methods of Identity Authentication." Economics. Law. Innovaion, no. 1 (March 30, 2021): 85–90. http://dx.doi.org/10.17586/2713-1874-2021-1-85-90.

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The paper substantiates the need to form the legal foundations of biometric authentication methods. The development of modern society is connected with the protection of information and information security. Biometric authentication methods are of interest as an innovative technology, the use of which should have an appropriate legal basis. The implementation of the necessary software must be regulated by separate provisions of copyright law. The use of biometric parameters of a person must also be subject to legal acts. The separation of the two concepts of authentication and identification in the legal aspect is justified. This is due to the fact that the possibility of access to both personal data of the individual and their financial assets is affected, since the main sphere of use of biometric parameters is the receipt of public and banking services. The regulatory legal acts that determine the possibility of using biometric authentication methods are considered. The paper draws practical conclusions about the need to distinguish an independent legal category of biometric personal data in the Russian legislation. To fix that the information about the subject of personal data will be attributed to biometric identification parameters if it is used for the purpose of establishing identity.

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Crampton, Jeremy W. "Platform Biometrics." Surveillance & Society 17, no. 1/2 (March 31, 2019): 54–62. http://dx.doi.org/10.24908/ss.v17i1/2.13111.

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This article identifies and analyses the emergence of platform biometrics. Biometrics are measurements of behavioral and physical characteristics, such as facial expressions, gait, galvanic skin response, and palm or iris patterns. Platform biometrics not only promise to connect geographically distant actors but also to curate new forms of value. In this piece I describe Microsoft Face, one of the major facial biometric systems currently on the market; this software promises to analyze which of seven “universal” emotions a subject is experiencing. I then offer a critique of the assumptions behind the system. First, theories of emotion are divided on whether emotions can be reliably and measurably expressed by the face. Second, emotions may not be universal, nor are there likely only seven basic emotions. Third, I draw on the work of Rouvroy and Berns (2013) to identify emotion-recognition technologies as a classic example of algorithmic governance. To outcome algorithmic governance is to enable the subject to creation and govern surveillance. Platform biometrics will therefore provide a key component of surveillance capitalism’s appropriation of human experience (neuro-liberalism).

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Asthana, Rajesh, Gurjit Singh Walia, and Anjana Gupta. "A novel biometric crypto system based on cryptographic key binding with user biometrics." Multimedia Systems 27, no. 5 (March 11, 2021): 877–91. http://dx.doi.org/10.1007/s00530-021-00768-8.

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Lagou, Panagiota, and Gregory Chondrokoukis. "Choosing a Biometric for Nonrepudiation." Information Security Journal: A Global Perspective 20, no. 1 (February 11, 2011): 17–24. http://dx.doi.org/10.1080/19393555.2010.544700.

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Baig, Asim, Ahmed Bouridane, Fatih Kurugollu, and Badr Albesher. "Cascaded multimodal biometric recognition framework." IET Biometrics 3, no. 1 (March 2014): 16–28. http://dx.doi.org/10.1049/iet-bmt.2012.0043.

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Barni, Mauro, Giulia Droandi, Riccardo Lazzeretti, and Tommaso Pignata. "SEMBA: secure multi‐biometric authentication." IET Biometrics 8, no. 6 (August 23, 2019): 411–21. http://dx.doi.org/10.1049/iet-bmt.2018.5138.

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