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.

The area of computer vision has been making big improvements in the latest decades, equally so has the area of electronics and small computers improved. These areas together have made it more available to build small, standalone systems for object detection in live video. This project's main objective is to examine whether a small device, e.g. Raspberry Pi 3, can manage an implementation of an object detection algorithm, called Viola-Jones, to count the occupancy of sitting people in a room with a camera. This study is done by creating an application with the library OpenCV, together with the language C+ +, and then test if the application can run on the small device. Whether or not the application will detect people depends on the models used, therefore three are tested: Haar Face, Haar Upper body and Haar Upper body MCS. The library's object detection function takes some parameters that works like settings for the detection algorithm. With that, the parameters needs to be tailored for each model and use case, for an optimal performance. A function was created to find the accuracy of different parameters by brute-force. The test showed that the Haar Face model was the most accurate. All the models, with their most optimal parameters, are then speed-tested with a FPS test on the raspberry pi. The result shows whether or not the raspberry pi can manage the application with the models. All models could be run and the Haar face model was fastest. As the system uses cameras, some ethical aspects are discussed about what people might think of top-corner cameras.

This paper is dealing with the detection clothes characteristics in the picture, for the use of person identification. These characteristics are described and categorized. It also deals with the design of the database structure, which works with masks and categories of characteristics for their processing. This work uses haar cascades to detect face and to determine the position of clothing for the purpose of color detection