Research of the effect of the room's illumination conditions on the facial detection and recognition algorithms

On the stage of the design of any video surveillance system, important parameters are the technical characteristics of the equipment and the conditions of its placement. The focus of this research is to investigate the effect of the illumination conditions and camera position on the quality of face detection and recognition algorithms for biometrical video surveillance system with facial recognition function. <br/> <br/> DIALux software was used for modelling the room with camera and simulate illumination conditions in different zones. For the analysis, the YOLOv8 neural network architecture, widely used for detection, trained on several datasets and standard recognition models from the DeepFace library, were used. Other parameter that became the subject of this research was the camera position (front and tilted), which also has a significant impact on the face detection and recognition algorithms results. As a rule, recognition libraries have strict requirements for the maximum values of the distance from camera to the recognition object, the maximum angle of the camera and its position relative to the visitor's face. Without knowledge of these limitations, it is impossible to design a video surveillance system with object recognition function. <br/> <br/> The research results demonstrates that the level of illumination significantly affects the quality of the algorithms. There are also maximum permissible camera angles at which it becomes impossible to detect and recognize faces. The first step in designing a facial recognition system should be to study the conditions of its placement (illumination conditions) and the characteristics of the equipment (camera resolution, focal length, angle of view).