CyberLink Partners with QNAP to Develop a Smart Facial Recognition Solution for Surveillance and Security

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CyberLink Partners with QNAP to Develop a Smart Facial Recognition Solution for Surveillance and Security-01

CyberLink Corp. (5203.TW), a pioneer in AI and facial recognition technologies, announced a partnership with QNAP® Systems Inc., which integrates CyberLink’s FaceMe® facial recognition engine into QNAP’s QVR Face and QVR Face Link smart facial recognition solutions. By leveraging CyberLink’s FaceMe® SDK, QVR Face Link can accurately recognize people’s faces, even when wearing a mask, with an accuracy rate of up to 98%.

QNAP is a market leader for NAS (Network Attached Storage), addressing the storage requirements of consumers and businesses of all sizes. On top of its cutting-edge hardware products, QNAP provides various value-added applications that expands NAS into areas such as surveillance, in which case the device is used as a NVR (Network Video Recorder).

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Through the integration of CyberLink’s FaceMe® AI facial recognition engine, QNAP’s QVR Face Link can identify visitors by monitoring the live video feed of connected IP cameras, with up to 98% accuracy rate, even when they wear a mask. The integration of FaceMe® into QNAP’s QVR Face Link, enables a number of frictionless, accurate, and instantaneous functionalities such as visitor and employee management, access control, or traffic analysis in any type of facility from offices to factories, and residential buildings to retail stores. Additionally, QVR Face Link’s API allows developers to integrate third-party services, such as door lock control, time keeping, and attendance records.

“Facial recognition is one of the most transformative technologies of our time, and it is finding its way into a diverse and fast growing range of applications. FaceMe®, one of the world’s leading facial recognition engines, which includes mask wearing detection and can recognize people with up to 98% accuracy when wearing a mask over their nose and mouth, has been integrated into many security, access control and health screening solutions that have helped in the fight against Covid-19 and are now part of the new normal,” said CyberLink. ”It is our honor to work with QNAP, by integrating FaceMe®, our AI-based facial recognition technology, into QNAP’s QVR Face Link, enabling contactless access control and a set of people detection and recognition features.”

“QVR Face Link performs facial recognition and analysis on a NAS without the need to upload data to the cloud for further processing. This greatly enhances user privacy without impacting result accuracy – even if the person is wearing a mask,” said Hanz Sung, product manager of QNAP, adding “The solution is suitable for door access systems or where non-contact identification is required. QVR Face Link can also be combined with services like QNAP QVR Pro for recording content management services or with QVR DoorAccess to become an access control and attendance management systems to effectively reduce the risk of contact infection by having to remove masks for facial identification.”

CyberLink’s FaceMe® SDK is a cross-platform AI facial recognition engine and ranked as one of the most accurate in the world. Offering comprehensive operating systems support and optimized for IoT hardware and AI inference engines, FaceMe® provides IoT/AIoT developers and system integrators highly accurate and flexible facial recognition capabilities, enabling a wide range of solutions applicable across industries.

CyberLink’s FaceMe® engine is ranked as one of the most accurate facial recognition technologies globally by the renowned National Institute of Standards and Technology’s Facial Recognition Vendor Test (FRVT), listed top 6 in both 1:1 and 1:N tests. With the comprehensive support for operating systems, including Windows, Linux (Ubuntu, RedHat, CentOS), JetPack (Jetson), iOS and Android, and the optimization for CPU, GPU, SoC, APU, and VPUs, FaceMe® provide a flexible solution to enable facial recognition across a wide range of IoT/AIoT devices.

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