Neurotechnology introduces new facial recognition algorithm

June 1, 2016 - 

Neurotechnology has released its new VeriLook face recognition algorithm, which, according to the company, provides five times higher accuracy in identifying full frontal faces and 10 to 15 times higher accuracy for unconstrained facial recognition.

VeriLook also includes a new face verification component for easy integration and use in authentication tasks such as user verification for mobile banking transactions. VeriLook is included in the new MegaMatcher 9.0 line of biometric software development kits (SDK) which includes fingerprint, face, iris, palmprint and voiceprint identification technologies that can be used in any combination for multi-biometric solutions.

“With this new version our development team focused on face recognition in real-world, unconstrained environments,” said Dr. Justas Kranauskas, project lead for Neurotechnology. “We achieved a ten-fold accuracy improvement on faces captured in lower resolution, with complex illumination, expressions and head rotations. This enabled us to offer a new face verification component which greatly simplifies user authentication by face, especially in mobile applications, while also enabling the face recognition algorithm to be used for complex 1:N identification,” Kranauskas added.

According to a company statement, improvements to the face recognition algorithm makes the product significantly easier to use and apply to a much broader range of face recognition applications, such as conducting automated facial image searches in large databases without the need for manual review. Faster face detection and more accurate estimation of facial attributes, including gender, smile, closed eyes, open mouth, glasses and dark glasses are also included. The new facial landmarks detection and tracking capabilities are more robust in a wider range of facial poses. Optional liveness detection determines if the system is viewing a live person or a photograph. A specialized API simplifies integration into a variety of solutions, and component pricing makes the face verification component economical for large-scale deployments on millions of devices.

Additional updates to the MegaMatcher 9 line include an enhanced iris algorithm that improves extraction speed and iris segmentation quality. It can accurately detect eyelids and can be used to locate irises in images that are captured in both the NIR range and in visible light. The iris image quality estimation is adapted to ISO/IEC 29794-6:2015 standard.

In May, Neurotechnology released the MegaMatcher Automated Biometric Identification System (ABIS), a comprehensive system designed for deploying large-scale multi-biometric projects.

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About Stephen Mayhew

Stephen Mayhew is the publisher and co-founder of Biometrics Research Group, Inc.. His experience includes a mix of entrepreneurship, brand development and publishing. Stephen attended Carleton University and lives in Toronto, Canada. Connect with Stephen on LinkindIn.