VeriLook Surveillance 2.0 SDK Advances Face Recognition for Large, Multi-Camera Video Surveillan... ( VILNIUS Lithuania Dec. 21 2011 /-...)

VeriLook Surveillance 2.0 SDK Advances Face Recognition for Large, Multi-Camera Video Surveillance Systems

Date:12/21/2011

VILNIUS, Lithuania, Dec. 21, 2011 /PRNewswire/ -- Neurotechnology, a provider of high-precision biometric identification technologies, today announced the availability of VeriLook Surveillance 2.0, a software development kit (SDK) for biometric face identification using live video streams from single or multiple high-resolution digital surveillance cameras. VeriLook Surveillance 2.0 provides real-time identification of faces and can be used in a wide range of surveillance systems for retail and commercial areas, entrance monitoring and counting, automated time-attendance systems, law enforcement applications and transportation security.

The new, integrated face tracking algorithm in VeriLook Surveillance 2.0 includes a robust, dynamic face model which can adapt to visual appearance changes as subjects move across the scene. It continues tracking of subjects even when their faces briefly disappear from the frame or when they are partially blocked by other objects or even other faces (a common problem while tracking multiple faces). Because it can now simultaneously process video streams from multiple surveillance cameras, VeriLook Surveillance 2.0 is suitable for use in large surveillance systems.

"VeriLook Surveillance 2.0 SDK now tracks faces with higher tolerance for lighting conditions, face poses and occlusions in multiple live video streams simultaneously," said Dr. Justas Kranauskas, project leader for Neurotechnology. "Together with real-time face recognition, automatic watch-list synchronization, flexible operator alerts and event logging implementation, VeriLook Surveillance adds value to any video surveillance application."

VeriLook Surveillance 2.0 incorporates the VeriLook 5.1 face recognition algorithm, which enables detection of faces with up to 45 degrees out-of-plane rotation in yaw angle. The new face tracking algorithm uses motion prediction models to re-localize faces that have undergone full occlusion, such as when a
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