Improving the Modelling of Robot Bunker with Camera

This study proposed an improvement on the model of robot bunker with camera. This is designed in order that robot is difficult to steal. The previous model is equipped with a security system. However, the system is not equipped with a camera so that when theft occurs, the action cannot be recorded. This study used 16 rules, because of the addition of variable pixels produced by the camera. The simulation is carried out as many as 30 (thirty) possible conditions of actions taken by the people on the robot with Matlab Fuzzy Toolbox. In the result of the simulations, the test results can change from safe conditions to alert or dangerous conditions. This is caused by changes in the number of pixels. The pixel value increases when someone tries to take a robot from the robot bunker. Thus the proposed model is more sensitive in detecting changes that occur around the robot bunker. Therefore this model can be applied in securing/protecting robot from theft

Survey on face detection methods

Face detection has attracted attention from many researchers due to its wide range of applications such as video surveillance, face recognition, object tracking and expression analysis. It consists of three stages which are preprocessing, feature extraction and classification. Firstly, preprocessing is the process of extracting regionsfrom images or real-time web camera, which then acts as a face or non-face candidate images. Secondly, feature extraction involves segmenting the desired features from preprocessed images. Lastly, classification is a process of clustering extracted features based on certain criteria. In this paper, 15 papers published from year 2013 to 2018 are reviewed. In general, there are seven face detection methods which are Skin Colour Segmentation, Viola and Jones, Haar features, 3D-mean shift, Cascaded Head and Shoulder detection (CHSD), and Libfacedetection. The findings show that skin colour segmentation is the most popular method used for feature extraction with 88% to 98% detection rate. Unlike skin colour segmentation method, Viola and Jones method mostly comprise of face regions and other parts of human body with 80% to 90% detection rate. OpenCV, Python or MATLAB can be used to develop real-life face detection system