Infrared Thermography and Big Data for Detection of People with Fever and Determination of High-Risk Areas in Epidemic Situations

Technological advances in computer science and their application in our daily life allow us to improve our understanding of problems and solve them effectively.A system design to detect people with fever and determine highrisk areas using infrared thermography and big data is presented.In order to detect people with fever,face detection algorithms of Viola-Jones and Kanade-Lucas are investigated,and comparison between them is presented using a training set of 406 thermal images and a test set of 2072 thermal images.Thermography analysis is performed on detected faces to obtain the temperature level on Celsius scale.With this information a sample database is created.To perform big data experimental analysis,Power Bi tool is used to determine the high-risk area.The experimental results show that Viola-Jones algorithm has a higher performance recognizing faces of thermal images than KanadeLucas,having a high detection rate,less false-positives rate and false-negatives rate.