Multimodal Biometric Systems for Personal Identification and Authentication using Machine and Deep Learning Classifiers

Multimodal biometrics, using machine and deep learning, has recently gained interest over single biometric modalities. This interest stems from the fact that this technique improves recognition and, thus, provides more security. In fact, by combining the abilities of single biometrics, the fusion of two or more biometric modalities creates a robust recognition system that is resistant to the flaws of individual modalities. However, the excellent recognition of multimodal systems depends on multiple factors, such as the fusion scheme, fusion technique, feature extraction techniques, and classification method. In machine learning, existing works generally use different algorithms for feature extraction of modalities, which makes the system more complex. On the other hand, deep learning, with its ability to extract features automatically, has made recognition more efficient and accurate. Studies deploying deep learning algorithms in multimodal biometric systems tried to find a good compromise between the false acceptance and the false rejection rates (FAR and FRR) to choose the threshold in the matching step. This manual choice is not optimal and depends on the expertise of the solution designer, hence the need to automatize this step. From this perspective, the second part of this thesis details an end-to-end CNN algorithm with an automatic matching mechanism. This thesis has conducted two studies on face and iris multimodal biometric recognition. The first study proposes a new feature extraction technique for biometric systems based on machine learning. The iris and facial features extraction is performed using the Discrete Wavelet Transform (DWT) combined with the Singular Value Decomposition (SVD). Merging the relevant characteristics of the two modalities is used to create a pattern for an individual in the dataset. The experimental results show the robustness of our proposed technique and the efficiency when using the same feature extraction technique for both modalities. The proposed method outperformed the state-of-the-art and gave an accuracy of 98.90%. The second study proposes a deep learning approach using DensNet121 and FaceNet for iris and faces multimodal recognition using feature-level fusion and a new automatic matching technique. The proposed automatic matching approach does not use the threshold to ensure a better compromise between performance and FAR and FRR errors. However, it uses a trained multilayer perceptron (MLP) model that allows people’s automatic classification into two classes: recognized and unrecognized. This platform ensures an accurate and fully automatic process of multimodal recognition. The results obtained by the DenseNet121-FaceNet model by adopting feature-level fusion and automatic matching are very satisfactory. The proposed deep learning models give 99.78% of accuracy, and 99.56% of precision, with 0.22% of FRR and without FAR errors. The proposed and developed platform solutions in this thesis were tested and vali- dated in two different case studies, the central pharmacy of Al-Asria Eye Clinic in Dubai and the Abu Dhabi Police General Headquarters (Police GHQ). The solution allows fast identification of the persons authorized to access the different rooms. It thus protects the pharmacy against any medication abuse and the red zone in the military zone against the unauthorized use of weapons

Fair disclosure of inside information by listed companies: A comparative study between the UK and Kuwait

In Kuwait, the Capital Markets Act 2010 (the Act) gives the regulatory authority the power to pass disclosure rules. However, the Act does not mention how to improve such disclosure rules. Therefore, this article will be discussed the aspect of protecting individual investors, which involves ensuring fair disclosure by listed companies, because informed investors are protected investors. This article will discuss the idea of having fair disclosure to protect investors to ensure that all investors have equal opportunity to access and know about inside information in an appropriate time and manner. This article also will be examined the existing disclosure rules that apply to equity shares in Kuwait as compared to the UK’s disclosure regimes as examples of developed countries.   Keywords: Disclosure rules, Inside Information, Kuwait securities market. &nbsp

Protecting Individual Investors under Kuwaiti Securities Law A comparative study

The financial market in Kuwait has existed for longer than the financial markets in the other Gulf countries. However, there has been limited regulation of stock exchange activities. This gap in the legislation was highlighted in the Suq al-Manakh crisis, when the absence of regulation resulted in heavy losses for large and small investors. This led the Government of Kuwait to enact a series of Acts from the late 1970s to 2010. The securities market was built around this legislation, which helped to stimulate the economy by attracting investors. However, the practical application of these laws brought to light some shortcomings in the regulation of the stock exchange and specifically the need for the legal protection of investors against the risk of loss due to market abuse (manipulation and insider dealing) of securities, irresponsible actions or poor corporate governance by firms. This research will trace the historical development of the legislation relating to the stock exchange up to the enactment of the new law (The Kuwait Capital Markets Act 2010 No. 7). The latter will be compared with similar legislation in the other markets of the GCC (as well as those in the USA and the UK when necessary) in order to evaluate its potential effectiveness in averting future problems and failures such as those that impacted Kuwait when it faced a financial crash in the early 1980s. Hence, the main aim of this thesis is to evaluate the extent to which the Kuwaiti securities legislation (the Act) is effective in protecting individual investors in terms of insider dealing, unfair disclosure and poor corporate governance by the issuer of the securities., and to suggest any amendments. Apart from this aim, the thesis will hopefully help to improve the knowledge of the Kuwaiti people about securities and it is also hoped that the research will be a useful addition to the body of literature in this field and will open a new avenue of research for other Kuwaiti students to follow for the improvement and development of the national economy

A distributed task allocation algorithm for a multi-robot system in healthcare facilities

Various ambient assisted living (AAL) technologies have been proposed for improving the living conditions of elderly people. One of them is to introduce robots to reduce dependency on support staff. The tasks commonly encountered in a healthcare facility such as a care home for elderly people are heterogeneous and are of different priorities. A care home environment is also dynamic and new emergency priority tasks, which if not attended shortly may result in fatal situations, may randomly appear. Therefore, it is better to use a multi-robot system (MRS) consisting of heterogeneous robots than designing a single robot capable of doing all tasks. An efficient task allocation algorithm capable of handling the dynamic nature of the environment, the heterogeneity of robots and tasks, and the prioritisation of tasks is required to reap the benefits of introducing an MRS. This paper proposes Consensus Based Parallel Auction and Execution (CBPAE), a distributed algorithm for task allocation in a system of multiple heterogeneous autonomous robots deployed in a healthcare facility, based on auction and consensus principles. Unlike many of the existing market based task allocation algorithms, which use a time extended allocation of tasks before the actual execution is initialised, the proposed algorithm uses a parallel auction and execution framework, and is thus suitable for highly dynamic real world environments. The robots continuously resolve any conflicts in the bids on tasks using inter-robot communication and a consensus process in each robot before a task is assigned to a robot. We demonstrate the effectiveness of the CBPAE by comparing its simulation results with those of an existing market based distributed multi-robot task allocation algorithm and through experiments on real robots

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses

Abstract The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work—a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students’ perspectives regarding the use of such tools in school work, and educators’ perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors’ institution, to discern students’ and educators’ perceptions of ChatGPT’s use in school work. We find that ChatGPT’s performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT’s use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks

Measuring the predictability of life outcomes with a scientific mass collaboration

<jats:p>How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.</jats:p&gt