Smart Interrogation System by Detection of Visual Focus of Attention

Abstract

The paper presents an approach to detect and control the focus of attention of the suspect using his/her eye gaze and head movement direction to build up an automatic interrogation system- specially to detect lies. To this point, we classified interrogation conversation into different criteria and identified the fatal ones. At first, we conducted psychological experiments on the sampled population to detect the different parameters connected with various symptoms when the suspect tells lies and build our knowledgebase with the results. This knowledgebase helps the system to make strategic decisions and to optimize accuracy. A monitoring camera captures continuous interrogation and feeds the frames to our proposed system. 3D head tracker is used to track the head from image and Active Shape Model (ASM) is utilized to localize face points. Vector Field of Image Gradient (VFIG) is calculated to track the eyeball and its rotation within the eye area. Random eye and head movement, change of eyebrow at the critical level of questionnaire provide us the possibility of detecting lies. Finally, experiments are conducted in a controlled environment to validate our psychological findings.