Background: With the rise of digitalization, there has been an increasing needfor secure and effective identification solutions, particularly in the realm of votingsystems. Facial biometric technology has emerged as a potential solution to combat fraud and improve the transparency and security of the voting process. Two well known facial identification algorithms, Local Binary Pattern Histograms (LBPH) and Eigenfaces, have been extensively used in computer vision for facial identification.However, their effectiveness in the context of a smart voting system is still a matter of debate. Objectives: The aim of this project is to compare the effectiveness of LBPH and Eigenfaces algorithms in the development of a smart voting system using the Haar cascade for face detection. The objective is to identify the more suitable approach between the two algorithms, considering factors such as lighting conditions and the facial expressions of the individuals being identified. The goal is to evaluate the algorithms using various metrics such as accuracy, precision, recall, and F1 score. Methods: The project involves the comparison of facial identification algorithms using the Haar cascade for face detection. Both the LBPH and Eigenfaces algorithms are implemented and evaluated in a complex environment that is similar to a polling station. The algorithms are trained and tested using a dataset of facial images with varying lighting conditions and facial expressions. The evaluation metrics, including accuracy, precision, recall, and F1 score, are used to compare the performance of thetwo algorithms. Results: The results of the project indicate that the LBPH algorithm performs better than Eigenfaces in terms of accuracy and performance. The algorithms havebeen tested with faces and objects in low-light conditions. Their accuracy and performance are also measured. Conclusions: The comparison of LBPH and Eigenfaces algorithms using the Haarcascade for face detection reveals that LBPH is a more suitable approach. The comparison of facial identification-based algorithms can significantly contribute to the voting process, thereby ensuring integrity of the voting process. The findings of this project can contribute to the development of a more reliable and secure voting system, and the evaluation metrics used in this project can be applied to future research in the field of facial identification purposes.

As robot technologies develop, many researchers have tried to use robots to support education. Studies have shown that robots can help students develop problem-solving abilities. Robotics technology is being increasingly integrated into the field of education largely due to the appealing image of robot’s young students have. With the rapid development of robotics, it has become feasible to use an educational robot for enhancing learning. This thesis explores the possibility of using robots as an educational tool for being quiz assistant in the class. Here we will be working with the humanoid-like robot and we will teach the robot to be a quiz assistant. The main purpose of this thesis is to have quizzes adapted to an individual knowledge of students in the class. By doing this a teacher can track a student’s performance individually while students will get the performance results as feedback using paper quizzes. When implemented fully, quizzes will be printed, distributed to students, collected from them, corrected, and students will be individually informed by email automatically and rapidly. Conceptually, this is a new approach to learning since frequent, paper-based quizzes become a learning tool in the service of active learning as opposed to their classical use, infrequently used control tool. The thesis scope is limited to contribute to individualization, distribution, and collection of the quizzes, leaving out the automatic correction. This is because for the latter there are already implemented solutions. With individualization, we mean identification of a student taking a certain quiz and conversely, deducing the identity of a student from a collected quiz. For this, we will use face detection and face recognition techniques. To this effect, an algorithm based on the technique Haar cascade by Viola and Jones [1]was used for face detection and Local Binary Pattern Histogram [from now on calledLBPH] method was used for face recognition [2]. This combination is shown to be, precise and maximally avoids illumination problems. The thesis also marks important details missing in the aforementioned paper as well as some drawbacks of the proposed technique. Our results show that RAQUEL system can perform face detection and recognition effectively by identifying and depending on the chosen interfacing strategy, then voicing identification details such as names, individual quiz number and seating row number of the students. Our system can not only be used to identify and bind a student identity to a certain quiz number, but also it can detail class/quiz attendance and keep track of in what order students gave back the quiz papers, helping to assure by biometric identification, that the automatically corrected quiz results are registered for correct student identity.