Introduction - If you have any usage issues, please Google them yourself
The demand for robust face recognition in real-world
surveillance cameras i ncreasing due to the needs of practical applications
such as security and surveillance. Although face recognition
has been studied extensively in the literature, achieving good
performance in surveillance videos with unconstrained faces i nherently
difficult. During the image acquisition process, the noncooperative
subjects appear in arbitrary poses and resolutions in
different lighting conditions, together with noise and blurriness
of images. In addition, multiple cameras are usually distributed
in a camera network and different cameras often capture a subject
in different views. In this paper, we aim at tackling this unconstrained
face recognition problem and utilizing multiple cameras
to improve the recognition accuracy using a probabilistic approach.
We propose a dynamic Bayesian network to incorporate
the information different cameras as well as the temporal
clues frames in a video sequence