3D Facial Action Units Recognition for Emotional Expression

units (AUs) when a facial expression is shown by a human face. This paper presents the methods to recognize AU using a distance feature between facial points which activates the muscles. The seven AU involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that characterizes a happy and sad expression. The recognition is performed on each AU according to the rules defined based on the distance of each facial point. The facial distances chosen are computed from twelve salient facial points. Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation and testing phase. By using any SVM kernels, it is consistent that AUs that are corresponded to sad expression has a high recognition compared to happy expression. The highest average kernel performance across AUs is 93%, scored by quadratic kernel. Best results for NN across AUs is for AU25 (Lips parted) with lowest CE (0.38%) and 0% incorrect classification

Modelling of Crowd Evacuation with Communication Strategy Using Social Force Model

Mobile crowd steering application has received much attention nowadays to steer crowds during fire or disaster evacuation. As reported in many studies, real drill experiments have been conducted to validate the mobile crowd steering application. However, simulations have also been undertaken to overcome the limitation of practical drill experiments when testing the application. Although there are algorithms reported for agent-based mobile crowd simulations, not many studies have adopted the social notion during mobile crowd steering simulations. As mobile crowd steering applications require user interaction during fire evacuation, we have foreseen a gap in current simulation algorithms, which leads to unrealistic simulation. This paper introduced a new insight into the agent-based crowd simulation through integration of communication strategy into the state of the art of social force for crowd management. The model was presented, formulated, and validated through a fire evacuation simulation. From the simulation results, the proposed model can reduce evacuation time and crowd density at the door opening area as compared to the original Social Force Model in a similar experimental setup

A Multi-Agent Simulation Evacuation Model Using The Social Force Model : A Large Room Simulation Study

Research on evacuation simulation has received significant attention over the past few decades. Disasters, whether they were caused by nature or by humans, which claimed lives were also the impetus for the establishment of various evacuation studies. Numerous research points to the possibility of simulating an evacuation utilizing the Social Force Model (SFM) and a leading person or leader, but without using the multi-agent architecture. Within the scope of this article, the multi-agent architecture for crowd steering that we suggest will be investigated. The architecture will utilize a model known as the Social Force Model to figure out how evacuees will move around the area. After this step, the model is simulated in NetLogo to determine whether the architecture can model the evacuation scenario. A simulation test is carried out for us to investigate the degree to which the behavior of the original SFM and the message-passing model is comparable to one another. The result demonstrates that the proposed architecture can simulate the evacuation of pedestrians. In addition, the simulation model can simulate utilizing the grouping strategy as well as the no grouping technique. The findings also showed that the model can capture many evacuation patterns, such as an arch-shaped pattern at the opening of the exit