Figure 2 - uploaded by Naser Damer
Content may be subject to copyright.
3: a high level structure of a multi-biometric identification system using score-level fusion. Comparison results are produced by different algorithms and modalities then normalized. The sets of normalized scores feed the fusion algorithm to produce a final set ranked by fused comparison scores. The algorithms block encapsulates quality assessment, feature extraction, and template comparison.
Source publication
![Figure 2.2: multi-biometric fusion at different levels [JRP04].](profile/Naser-Damer/publication/325488820/figure/fig4/AS:632684187504640@1527855073205/multi-biometric-fusion-at-different-levels-JRP04_Q320.jpg)
Biometric recognition is the automated recognition of individuals based on their behavioral or biological characteristics. Beside forensic applications, this technology aims at replacing the outdated and attack prone, physical and knowledge-based, proofs of identity. Choosing one biometric characteristic is a tradeoff between universality, acceptab...
Similar publications
Exploiting the potential of space-borne oceanic measurements to characterize the sub-surface structure of the ocean becomes critical in areas where deployment of in situ sensors might be difficult or expensive. Sea Surface Temperature (SST) observations potentially provide enormous amounts of information about the upper ocean variability. However,...
Citations
... Biometric authentication is typically used to gain access on log-in to a system/service. This means that the rightful user is identified and recognised, but with an intruder, his or her data will be captured automatically and be sent to both the lawful user and the server administrator (Naser, 2018). Biological tracing such as fingerprint and face images, as well as biographic identification information that reflects surname; names; ...
... Biometric systems will provide secured access from when combined with gait cycle detection known as image detection, finger-print detection, voice-recognition and password encryption (Efrati et al., 2014;Naser, 2018;. A weakness of the authentication systems is that they can be manipulated (Holden, 2010;and De Groot, 2014). ...
This study is motivated by the Fourth Industrial Revolution (4IR) through the actual AA (AA) of enterprise application architecture (EAA) for supply chain management (SCM) in small and medium enterprises (SMEs). A technological acceptance model (TAM) is set out, and further discussed with a few theories such as; the theory of reasoned action (TRA), the theory of planned behaviour (TPB), and the diffusion theory of innovation (DTI) to outline the conceptual framework. The objective of the study is to determine both internal and external factors affecting the actual AA of enterprise application architecture (EAA). The study employed a quantitative research methodology through stratified random sampling selection from within certain strata within the population on the basis of common characteristics such as; SME ownership and managerial competencies. The study adopted both diagnostic tests regarding normal distribution tests, validity presented through the Kolmogorov-Smirnov test, and reliability test through Cronbatch’s Alpha. Most importantly, data was analysed through Pearson correlations, Pearson coefficients, analysis of variance (ANOVA), linear regressions, and multilinear regressions. Overall, positive associations were established among independent variables, predictor variables, and response variables. This study contributes to the embryonic literature on fostering SMEs’ perspectives on the AA of EAA for SCM, particularly in the Capricorn District Municipality of Limpopo Province in South Africa. The results provide evidence that the internal and external factors affect the AA of EAA with positive slopes, except perceived attitudes with a negative slope. The study recommends positive views to all internal and external stakeholders to raise awareness about five façades. (a) Inflexible legacy and monolithic system lock-in. (b) A fragmented application and software landscape. (c) The impacts of mission-critical app development on time-to-market. (d) Automation of siloed, manual, and partially digital business processes. (e) Scale, security, availability, and compliance.
... Likewise, the adoption of EAA considerations includes biometric technology authentication systems such as image detection, fingerprint detection, voice recognition and password encryption (Mayhew 2019;Naser 2021;Sanders et al. 2016). The extent to which employees validate TAM, affected by lack of financial resources, information systems components and working capital (Maverick 2021), thus, the construction of the underlying optimal control problem in a stochastic background in the adoption of EAA (Parise, Lygeros & Ruess 2015). ...
Background: This article inspects the internal constituents on employees’ aptitudes and trepidations (EATs) for the adoption of enterprise application architecture (EAA) for supply chain management (SCM) in small and medium enterprises (SMEs) within Capricorn District Municipality. EATs trait origins widespread destruction and inflict in the adoption of EAA if not managed amicably, unless, the enterprise accentuate the importance of the 4th Industrial revolution with careful planning to deal with the aftermath and mitigate the inspirations instinctively. Thus, the logistics of EAA adoption is one of the main activities in information technology (IT) that requires meticulous knowledge in algorithms for a successful SCM.
Objective: The principal objective of this study is to determine whether EATs affect the adoption of EAA for SCM in SMEs and to identify gaps and institute state of the art in research for future studies.
Method: This study adopts a descriptive research method with the use of 310 respondents targeted at both SME owners and managers, sculpted over quantitative research. Based on the analyses of data, the linear regression model is executed. As a result, the author realised that ‘EATs affect the adoption of EAA’ as a dominant research focus of these studies. In this study, the response EATs as a primary influencer of the adoption of EAA, considered on linear regression model for attainable SCM. In practice, SMEs should link EAA to new technologies, enterprise capabilities, objectives and strategies, as well as the existing culture and performance philosophies.
Results: The SMEs’ success in SCM activities is dependent on employees’ perceptions, attitudes, motivation and learning abilities that could ease the narratives on the adoption of EAA.
Conclusion: These results provide the following comprehensions for future research: applying simplicity by getting them on board, introducing EAA through different channels of learning, building a coalition of willing mindsets, motivating its purpose and celebrating small progress. The research provides a profound understanding relevant in the contextual background to SMEs to adopt EAA for SCM that depends on the enterprise resources (ERs) acquisition and\or availability.
Keywords: Coding and programming; employees’ aptitudes and trepidations; enterprise application architecture; information technology; motivation; perceptions; small and medium enterprises; supply chain management; technologically accepted models
... It derived from forensic investigations [Rho56] and evolved into several applications scenarios regarding security and convenience. The strong link between identities and individuals is used in security-based applications, such as forensics or border control, or in convenience-based applications, such as automatic log-in and smart home personalization [Dam18]. ...
... During enrolment, a subject is included in the database of the biometric system. The enrolment step includes providing a trusted identity, capturing the biometric characteristics, ensuring high quality of the capture, extracting a distinct template, and storing the templates with the associated identity information in a database [Dam18]. ...
... In identification mode, the system aims to assign an identity to an unknown subject based on its the captured biometrics (e.g. "Whose biometric data is this?") [Dam18]. It aims to recognize an individual by comparing its template against all enrolled templates. ...
Biometric verification refers to the automatic verification of a person’s identity based on their behavioural and biological characteristics. Among various biometric modalities, the face is one of the most widely used since it is easily acquirable in unconstrained environments and provides a strong uniqueness. In recent years, face recognition systems spread world-wide and are increasingly involved in critical decision-making processes such as finance, public security, and forensics. The growing effect of these systems on everybody’s daily life is driven by the strong enhancements in their recognition performance. The advances in extracting deeply-learned feature representations from face images enabled the high-performance of current face recognition systems. However, the success of these representations came at the cost of two major discriminatory concerns. These concerns are driven by soft-biometric attributes such as demographics, accessories, health conditions, or hairstyles. The first concern is about bias in face recognition. Current face recognition solutions are built on representation-learning strategies that optimize total recognition performance. These learning strategies often depend on the underlying distribution of soft-biometric attributes in the training data. Consequently, the behaviour of the learned face recognition solutions strongly varies depending on the individual’s soft-biometrics (e.g. based on the individual’s ethnicity). The second concern tackles the user’s privacy in such systems. Although face recognition systems are trained to recognize individuals based on face images, the deeply-learned representation of an individual contains more information than just the person’s identity. Privacy-sensitive information such as demographics, sexual orientation, or health status, is encoded in such representations. However, for many applications, the biometric data is expected to be used for recognition only and thus, raises major privacy issues. The unauthorized access of such individual’s privacy-sensitive information can lead to unfair or unequal treatment of this individual. Both issues are caused by the presence of soft-biometric attribute information in the face images. Previous research focused on investigating the influence of demographic attributes on both concerns. Consequently, the solutions from previous works focused on the mitigation of demographic-concerns only as well. Moreover, these approaches require computationally-heavy retraining of the deployed face recognition model and thus, are hardly-integrable into existing systems. Unlike previous works, this thesis proposes solutions to mitigating soft-biometric driven bias and privacy concerns in face recognition systems that are easily-integrable in existing systems and aim for more comprehensive mitigation, not limited to pre-defined demographic attributes. This aims at enhancing the reliability, trust, and dissemination of these systems. The first part of this work provides in-depth investigations on soft-biometric driven bias and privacy concerns in face recognition over a wide range of soft-biometric attributes. The findings of these investigations guided the development of the proposed solutions. The investigations showed that a high number of soft-biometric and privacy-sensitive attributes are encoded in face representations. Moreover, the presence of these soft-biometric attributes strongly influences the behaviour of face recognition systems. This demonstrates the strong need for more comprehensive privacy-enhancing and bias-mitigating technologies that are not limited to pre-defined (demographic) attributes. Guided by these findings, this work proposes solutions for mitigating bias in face recognition systems and solutions for the enhancement of soft-biometric privacy in these systems. The proposed bias-mitigating solutions operate on the comparison- and score-level of recognition system and thus, can be easily integrated. Incorporating the notation of individual fairness, that aims at treating similar individuals similarly, strongly mitigates bias of unknown origins and further improves the overall-recognition performance of the system. The proposed solutions for enhancing the soft-biometric privacy in face recognition systems either manipulate existing face representations directly or changes the representation type including the inference process for verification. The manipulation of existing face representations aims at directly suppressing the encoded privacy-risk information in an easily-integrable manner. Contrarily, the inference-level solutions indirectly suppress this privacy-risk information by changing the way of how this information is encoded. To summarise, this work investigates soft-biometric driven bias and privacy concerns in face recognition systems and proposed solutions to mitigate these. Unlike previous works, the proposed approaches are (a) highly effective in mitigating these concerns, (b) not limited to the mitigation of concerns origin from specific attributes, and (c) easily-integrable into existing systems. Moreover, the presented solutions are not limited to face biometrics and thus, aim at enhancing the reliability, trust, and dissemination of biometric systems in general.
... To infer complex human actions, richer context is required which can only be done by fusion of different sensor modalities. Integrating additional sensor or sensor categories can boost classification accuracy by achieving the following gains as reported in [151] and initially defined by Bellot et al. [152]: 1) Accuracy gain: accuracy of decisions and representations after the fusion process is improved. Noise and errors are reduced in comparison to single source information. ...
Sensors are devices that quantify the physical aspects of the world around us. This ability is important to gain knowledge about human activities. Human Activity recognition plays an import role in people’s everyday life. In order to solve many human-centered problems, such as health-care, and individual assistance, the need to infer various simple to complex human activities is prominent. Therefore, having a well defined categorization of sensing technology is essential for the systematic design of human activity recognition systems. By extending the sensor categorization proposed by White, we survey the most prominent research works that utilize different sensing technologies for human activity recognition tasks. To the best of our knowledge, there is no thorough sensor-driven survey that considers all sensor categories in the domain of human activity recognition with respect to the sampled physical properties, including a detailed comparison across sensor categories. Thus, our contribution is to close this gap by providing an insight into the state-of-the-art developments. We identify the limitations with respect to the hardware and software characteristics of each sensor category and draw comparisons based on benchmark features retrieved from the research works introduced in this survey. Finally, we conclude with general remarks and provide future research directions for human activity recognition within the presented sensor categorization.
