New Integrated Face Tracking Algorithm Adapts to Faces in Motion
Vilnius, Lithuania – December 21, 2011 – 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 subject has been fully obstructed by a wall and emerged on the other side. The dynamic face model allows the system to efficiently and reliably track faces in front of complex backgrounds and ensures that subjects can be localized in all video frames, even under strenuous conditions. Face images can then be matched against internal databases, such as criminal watch lists or authorized personnel, and VeriLook Surveillance will immediately report recognized faces to the system.
VeriLook Surveillance 2.0 enables one computer to process images from multiple cameras in the same VeriLook Surveillance process. In large, extended surveillance systems data may be coming from multiple cameras with processing occurring across an array of computers. The new VeriLook Surveillance SDK provides connections between VeriLook Surveillance units deployed on different computers, synchronizing the databases so that the system works as a whole within the logical network. When a new subject is enrolled in one of the surveillance instances, the data is sent to other surveillance processes, enrolling the subject in all running surveillances. The synchronization routines are provided as sample level code, leaving the customer free to modify the communication or logic behind any or all synchronization processes.
VeriLook Surveillance 2.0 SDK enables real-time face detection, extraction and matching by providing embedded parallelization of all VeriLook functions for improved performance on multi-core, multi-processor systems. VeriLook Surveillance supports a wide variety of programming languages and works with a wide range of high-resolution digital surveillance cameras.
VeriLook Surveillance 2.0 SDK enables the development of fast, reliable and cost-effective biometric facial identification systems for Windows and Linux platforms. VeriLook Surveillance templates are fully compatible with MegaMatcher multi-biometric technology.
VeriLook Surveillance 2.0 SDK is available through Neurotechnology or from distributors worldwide.
Neurotechnology is a provider of high-precision biometric fingerprint, face, iris, palmprint and voice identification algorithms, object recognition technology and software development products. More than 2500 system integrators, security companies and hardware providers integrate Neurotechnology’s algorithms into their products, with millions of customer installations worldwide.
Neurotechnology’s identification algorithms have consistently earned the highest honors in some of the industry’s most rigorous competitions, including the National Institute of Standards and Technology (NIST)’s Fingerprint Vendor Technology Evaluation (FpVTE), the Iris Exchange (IREX) and the Fingerprint Verification Competitions (FVC).
Drawing from years of academic research in the fields of neuroinformatics, image processing and pattern recognition, Neurotechnology was founded in 1990 in Vilnius, Lithuania and released its first fingerprint identification system in 1991. Since that time the company has released more than 60 products and version upgrades for identification and verification of objects and personal identity.
Jennifer Allen Newton
Bluehouse Consulting Group, Inc.
jennifer (at) bluehousecg (dot) com