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![Facial Image Sample with facial map for Facial Biometrics. Adapted from [5].](profile/Lubasi-Musambo/publication/325574625/figure/fig5/AS:634153695449091@1528205431843/Facial-Image-Sample-with-facial-map-for-Facial-Biometrics-Adapted-from-5.png)
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![Fig. 2. Fingerprint image sample [17].](profile/Lubasi-Musambo/publication/325574625/figure/fig2/AS:634153695453188@1528205431726/Fingerprint-image-sample-17_Q320.jpg)
![Fig. 5. Voice print. Adapted from [5].](profile/Lubasi-Musambo/publication/325574625/figure/fig4/AS:634153695473664@1528205431788/Voice-print-Adapted-from-5_Q320.jpg)
Facial biometrics captures human facial physiological data, converts it into a data item variable so that this stored variable may be used to provide information security services, such as authentication, integrity management or identification that grants privileged access or control to the owner of that data variable. In this paper, we propose a m...
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... biometrics divides into two aspects namely the face detection and face recognition programs. Face recognition extracts a face from a given image while face recognition compares a captured face against saved faces in order to match the face. The entire process is run by a series of complex algorithms. One of the options of face recognition is to select features of a face and match those features to a face. Fig. 6 below shows a facial image sample with facial image mapping that is used to collect facial features. The facial features or dataset is normally stored in a database. In ideal situations this database must be encrypted to achieve sufficient security ...
Context 2
... and face recognition programs. Face recognition extracts a face from a given image while face recognition compares a captured face against saved faces in order to match the face. The entire process is run by a series of complex algorithms. One of the options of face recognition is to select features of a face and match those features to a face. Fig. 6 below shows a facial image sample with facial image mapping that is used to collect facial features. The facial features or dataset is normally stored in a database. In ideal situations this database must be encrypted to achieve sufficient security ...
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Citations
... The resources required to regularly assess this information are limited. It would be incredibly difficult to tell if a user is genuine during a quiz if the person's data profile was not broad enough to reveal any patterns [147]. ...
... The proposed framework has combined the following features -continuous face detection and recognition, mouth open or close, gaze tracking for the entire duration of the exam[5] - [7]. In the preparation phase (before taking the exam), the test taker has to submit 15 -20 images with their registered number. ...
Different forms of remote education and massive online open courses are gaining reputation. The skill to proctor the online examination is a main essential factor for the scalability for promoting the students for next stage education. Existing manual monitoring is the most approaching method in education either by visually monitoring or by physically accompanying test takers to the examination centre and monitoring them. Learner’s identity verification and proctoring of online examinations is one of the main challenges in online learning systems. The migration and implementation of the online exam have been accelerated during the pandemic COVID-19. So the existing systems need a safe mechanism to authenticate and proctor online students. In this paper, we propose a system for providing the solution for authentication and proctor by using the different biometric technologies. The performance of the proposed system is evaluated by using real time videos with different scenario. The experimental results provide an improved accuracy than existing research works.
... Nowadays, although there are still some non-biometric based authentication approaches [20], the latest attempts for online student authentication automation tends to use biometric technologies; facial [21] [22] [23] [24] [25] [26], fingerprints [27] or typing [28], [29]. On the other hand, some approaches try some combination of them, such as face and voice [30] or face, voice and typing [31] [32]. ...
Identity verification and proctoring of online students are one of the key challenges to online learning today. Especially for online certification and accreditation, the training organizations need to verify that the online students who completed the learning process and received the academic credits are those who registered for the courses. Furthermore, they need to ensure that these students complete all the activities of online training without cheating or inappropriate behaviours. The COVID-19 pandemic has accelerated (abruptly in certain cases) the migration and implementation of online education strategies and consequently the need for safe mechanisms to authenticate and proctor online students. Nowadays, there are several technologies with different grades of automation. In this paper, we deeply describe a specific solution based on the authentication of different biometric technologies and an automatic proctoring system (system workflow as well as AI algorithms), which incorporates features to solve the main concerns in the market: highly scalable, automatic, affordable, with few hardware and software requirements for the user, reliable and passive for the student. Finally, the technological performance test of the large scale system, the usability-privacy perception survey of the user and their results are discussed in this work.
... There were some studies excluded from this review [48 -52] as they did not focus on types of authentication methods as a safety practice for information security. Of the 28 articles reviewed, 13 were based in India [20 -26], [30], [34 -37], [42]; four were from China [32], [33], [38], [44]; two from Saudi Arabia [18], [19] one from Poland [27]; one from the Czech Republic [31]; one from the United Arab Emirates [28]; one from Turkey [43]; one from Ukraine [40]; 1 from Jordan [17]; and one each from Zambia [29]; Philippines [39]; and the United States [41] (as shown in Figure 2). ...
... This study suggested using a multibiometric system for a more significant variability. 4) Facial authentication: Musambo and Phiri [29] have proposed a facial authentication scheme for university students. It was found that the system only obtained 66% detection rate. ...
... However, this authentication method may not be well received by cloud computing users. This is because this form of authentication has not been widely practiced and expensive [29]. A multifactor authentication combining fingerprint and iris was formulated [24]. ...
The authentication system is one of the most important methods for maintaining information security in smart devices. There are many authentication methods, such as password authentication, biometric authentication, signature authentication, and so on, to protect cloud users’ data. However, online information is not yet effectively authenticated. The purpose of this systematic literature review is to examine the current types of authentication methods as a safety practices for information security among Internet users. The PRISMA method was adopted to present a systematic literature review of 28 articles from three main databases (20 articles from Scopus, one article from Google Scholar, and seven articles from Dimension). This study used the Prediction Study Risk of Bias Assessment Tool to appraise the quality of the included studies. From the findings of the study, a total of three main themes were identified: password authentication, biometric authentication, and multiple-factor authentication. Multiple-factor authentication was found to be the most secure and most frequently recommended authentication method. It is highly recommended to implement three-factor authentication and multi-biometric model in the future, as it provides a higher surveillance level in terms of information security among cloud computing users.
... The FAW moth being considered under this study has very unique body features as shown in fig. 6. [20] demonstrated that these unique features can be used to identify and recognize an object. Our vision sensor will be placed on the top cover (lid) in order to give it a clear view of the FAW moth. ...
As farmers' struggle with unpredictable rainfall patterns, the horror of emerging crop pests that will likely affect the quality and quantity of their harvest does not spare them. Therefore, it is very important to protect the crop by monitoring these pests. In the 2016/2017 farming season, the Southern African Development Community (SADC) food security was threaten by the outbreak of the fall armyworm (FAW) despite the normal rainfall. The FAW affected several crops including maize, the staple food in the DRC, Botswana, Malawi, Namibia, Swaziland, South Africa, Zambia and Zimbabwe. In Zambia alone, attempts to control the fall armyworms that had affected approximately 130,000 hectares of crops costed about US$ 3 million. This led SADC in collaboration with Food and Agriculture Organization (FAO) to classify the outbreak as a threat to food and nutrition security, and livelihoods of smallholder farmers in the region. This has posed a number of challenges on the stakeholders such as the University of Zambia (UNZA) School of Agricultural Sciences and Department of Biological Sciences to come up with more precise and proactive measures for monitoring and controlling the pest. This paper proposes a study to develop an automated system for identifying and capturing images of the FAW moths in the field using a supervised machine learning (ML) technique called Convolutional Neural Network (CNN). The proposed study will aim to address current challenges that the UNZA School of Agricultural Sciences and Department of Biological Sciences are facing in monitoring the FAW using the pheromone traps. The pheromone trap will be modified to include a raspberry PI, vision sensor and motion sensor to be used for data collection. The system will further integrate GPRS and 3G/4G connectivity to allow automatic data collection without field visits while GPS receiver will enable automatic position awareness of the trap. The data collected will automatically be sent to the cloud servers for immediate analysis and access by the stakeholders.
Most developing countries in Africa have civil registration processes with inherent challenges that inhibit their efficiency and accuracy. Attempts at modernization and improvement are on-going. In this article, a biometric framework that can be used to mitigate the challenges inherent in biometric data management for the civil registration system in Zambia is presented. The proposed framework presented is based on ISO models and adapted for most developing countries with low Information and Communication Technologies. A baseline study was undertaken to assess the understanding and appreciation of biometrics within the Zambian community and how this data might be stored or managed. A study of the Zambian civil registration business process has been undertaken and the authors propose an automated civil registration business process. Results obtained show that currently, Zambia does NOT have a biometric framework that supervises use of biometric data. The proposed model once adopted would form a secure framework for civil registration based on biometrics and ISO standards.
