Face recognition has been regarded as an effective method for subject identification at a distance because of its covert and remote sensing capability. However, face images have a low resolution when they are captured at a distance (say, larger than 5 meters) thereby degrading the face matching performance. To address this problem, we propose an imaging system consisting of static and pan-tilt-zoom (PTZ) cameras to acquire high resolution face images up to a distance of 12 meters. We propose a novel coaxial-concentric camera configuration between the static and PTZ cameras to achieve the distance invariance property using a simple calibration scheme. We also use a linear prediction model and camera motion control to mitigate delays in image processing and mechanical camera motion. Our imaging system was used to track 50 different subjects and their faces at distances ranging from 6 to 12 meters. The matching scenario consisted of these 50 subjects as probe and additional 10 000 subjects as gallery. Rank-1 identification accuracy of 91.5% was achieved compared to 0% rank-1 accuracy of the conventional camera system using a state-of-the-art matcher. The proposed camera system can operate at a larger distance (up to 50 meters) by replacing the static camera with a PTZ camera to detect a subject at a larger distance and control the second PTZ camera to capture the high-resolution face image.

Face recognition at a distance is gaining wide attention in order to augment the surveillance systems with face recognition capability. However, face recognition at a distance in nighttime has not yet received adequate attention considering the increased security threats at nighttime. We introduce a new face image database, called Near-Infrared Face Recognition at a Distance Database (NFRAD-DB). Images in NFRAD-DB are collected at a distance of up to 60 meters with 50 different subjects using a near-infrared camera, a telescope, and near-infrared illuminator. We provide face recognition performance using FaceVACS, DoG-SIFT, and DoG-MLBP representations. The face recognition test consisted of NIR images of these 50 subjects at 60 meters as probe and visible images at 1 meter with additional mug shot images of 10,000 subjects as gallery. Rank-1 identification accuracy of 28 percent was achieved from the proposed method compared to 18 percent rank-1 accuracy of a state of the art face recognition system, FaceVACS. These recognition results are encouraging given this challenging matching problem due to the illumination pattern and insufficient brightness in NFRAD images.