Vast amounts of video footage are being continuously acquired by surveillance systems on private premises, commercial
properties, government compounds, and military installations. Facial recognition systems have the potential to identify
suspicious individuals on law enforcement watchlists, but accuracy is severely hampered by the low resolution of typical
surveillance footage and the far distance of suspects from the cameras. To improve accuracy, super-resolution can
enhance suspect details by utilizing a sequence of low resolution frames from the surveillance footage to reconstruct a
higher resolution image for input into the facial recognition system. This work measures the improvement of face
recognition with super-resolution in a realistic surveillance scenario. Low resolution and super-resolved query sets are
generated using a video database at different eye-to-eye distances corresponding to different distances of subjects from
the camera. Performance of a face recognition algorithm using the super-resolved and baseline query sets was calculated
by matching against galleries consisting of frontal mug shots. The results show that super-resolution improves
performance significantly at the examined mid and close ranges.
Conference Committee Involvement (5)
Biometric Technology for Human Identification V
18 March 2008 | Orlando, Florida, United States
Biometric Technology for Human Identification IV
9 April 2007 | Orlando, Florida, United States
Biometric Technology for Human Identification III
17 April 2006 | Orlando (Kissimmee), Florida, United States
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