Face Recognition in Multi-Camera Surveillance Videos

Recognizing faces in surveillance videos becomes difficult due to the poor quality of the probe data in terms of resolution, noise, blurriness, and varying light-ing conditions. In addition, the poses of probe data are usually not frontal view, contrary to the standard for-mat of the gallery data. The discrepancy between the two types of the data makes the existing recognition al-gorithm less accurate in real-world data. In this pa-per, we propose a multi-camera video based face recog-nition framework using a novel image representation called Unified Face Image (UFI), which is synthesized from multiple camera feeds. Within a temporal window the probe frames from different cameras are warped to-wards a template frontal face and then averaged. The generated UFI is a frontal view of the subject that in-corporates information from different cameras. We use SIFT flow as a high level alignment tool to warp the faces. Experimental results show that by using the fused face, the recognition performance is better than the re-sult of any single camera. The proposed framework can be adapted to any multi-camera video based recogni-tion method using any feature descriptors or classifiers. 1