Unified Face Representation for Individual Recognition in Surveillance Videos

Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 6)


Recognizing faces in surveillance videos becomes difficult due to the poor quality of the probe data in terms of resolution, noise, blurriness, and varying lighting conditions. In addition, the poses in the probe data are usually not frontal view, as opposed to the standard format of the gallery data. The discrepancy between the two types of data makes the existing recognition algorithm far less accurate in real-world surveillance video data captured in a multi-camera network. In this chapter, we propose a multi-camera video based face recognition framework using a novel image representation called Unified Face Image (UFI), which is synthesized from multiple camera video feeds. Within a temporal window the probe frames from different cameras are warped towards a template frontal face and then averaged. The generated UFI representation is a frontal view of the subject that incorporates information from different cameras. Face super-resolution can also be achieved, if desired. We use SIFT flow as a high level alignment tool to warp the faces. Experimental results show that by using the unified face image representation, the recognition performance is better than the result of any single camera. The proposed framework can be adapted to any multi-camera video based face recognition using any face feature descriptors and classifiers.


Face recognition Face registration in video Multi-camera network Surveillance videos 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Center for Research in Intelligent SystemsUniversity of CaliforniaRiversideUSA

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