A cognitive camera system for increasing face recognition performance in backlit conditions

Abstract

One of the most difficult challenges in counter-terrorism, crime fighting and surveillance mission is to accurately identify people from an image/video footage to catch shortlisted terrorists and criminals. For this purpose, imaging devices used in video surveillance systems are being improved in many aspects such as spatial resolution, frame rate, dynamic range, spectral characteristics in order to achieve better imaging performance for both monitoring and automatic detection/recognition tasks. These development efforts aim to improve the basic imaging characteristics of the device, such as the average amount of brightness/darkness of the video footage, irrespective of high-level semantic elements/knowledge such as presence and locations of monitored objects/individuals in the scene monitored by the camera. Nevertheless, strong local/global illumination variations in harsh environments result in high dynamic range in the scene and thus over-/underexposed regions in video frames. In this study, a cognitive imaging system prototype that will allow higher face recognition performance by taking into account the locations of automatically detected human faces in the scene and the local illumination conditions/dynamic range values is proposed with a developed/produced smart camera. This is achieved by developing a smart Auto-Exposure (AE) algorithm which use only the region of interest (ROI) to compute/measure the brightness of the video frame. The current ROI is selected by intelligently and sequentially traversing among the detected faces to effectively handle whole faces in the scene. The experimental results show that the proposed ”cognitive” camera can achieve a dramatic increase in accuracy performance over the normal camera in the face recognition task.