Facial recognition is becoming more popular in newer systems for access control — a shift that began before the pandemic and has intensified with a market shift toward “touchless” systems. A new facial recognition platform is emerging that responds to the access control industry’s increased interest in facial recognition by expanding the concept with a new higher level of technology. At the core of the new system is high-performance, true-3D sensing with facial depth map processing at low power consumption, which enriches the capabilities of small-footprint access control devices. New proficiencies include anti-spoofing (preventing the use of a 2D photo of an authorised user to gain entry) and anti-tailgating (preventing an unauthorised person from gaining entry by following an authorised user) in real time and in challenging lighting conditions. The system uses “true 3D sensing,” which incorporates single-camera structured-light 3D sensing—as opposed to dual-camera depth sensing or IR video imaging-based approaches. AI vision processing and 3D sensing technologies The new “Janus reference design” incorporates AI vision processing, 3D sensing technologies, and RGB-IR CMOS image sensor technologies from Ambarella, Lumentum and ON Semiconductor. Specifically, Lumentum’s high-reliability, high-density VCSEL projector for 3D sensing combines with ON Semiconductor’s RGB-IR CMOS image sensor and Ambarella’s powerful AI vision system on chip (SoC). The Ambarella, Lumentum, and ON Semiconductor engineering teams worked together to incorporate their complementary technologies into the reference design. A reference design offers OEM product and engineering teams a fully functional engineering reference implementation that they can use as the basis for their own product. Teams will often customise a reference design with their choice of various third-party hardware components to fit their product specifications and positioning. They might also integrate their own software, algorithms, and back-end system integrations. The advantage to this approach is that the manufacturer can get to market quickly with a next-generation product that emphasises their core strengths. 3D depth information for facial recognition Generally, it takes between nine months and a year for a manufacturer to get to market using a fully functional reference design, such as the one developed jointly by Ambarella, Lumentum and ON Semiconductor. The Janus platform leverages 3D depth information generated via structured light for facial recognition with a >99% recognition accuracy rate. Traditional 2D-based solutions are prone to false acceptance and presentation attacks, whereas 3D sensing delivers advanced security—just as mobile phones use true-depth cameras for facial recognition. 3D facial recognition also significantly reduces the gender and ethnic biases demonstrated by some 2D facial recognition solutions. The Janus reference design is also aimed at future smart locks for enterprise and residential use: its unique single-camera 3D sensing solution will help OEMs overcome cost and manufacturability barriers, while the ultra-low power edge AI capability can effectively extend the battery life, which in turn reduces maintenance cost. Video security and access control Ambarella sees touchless access control, as well as the convergence of video security and access control, as the mega-trends driving industry innovation and growth—using video, computer vision, and 3D sensing to not only address safety and security, but also to improve the user experience and public health, says William Xu, director of marketing for Ambarella. The convergence of video security cameras and access control readers has been widely discussed by leading access control OEMs. In many cases, they already integrate video security cameras, readers, door controllers, cloud-access, and the like. In most enterprise installations, one would typically find security cameras installed where there are access control readers. Combining the two devices significantly reduces the maintenance cost and system complexity. “In comparison to fingerprint or other contact-based approaches, Janus-based access control is touchless—requiring no physical contact with authentication hardware such as fingerprint sensors or keypads—reducing infection risk while enabling a seamless experience,” says Mr. Xu. “The Janus platform provides true 3D depth information to prevent unauthorised individuals from mimicking legitimate users, and the advanced embedded AI processor enables tracking and anti-tailgating algorithms. Janus-based devices perform well in challenging lighting conditions and they are capable of authenticating multiple users simultaneously, with imperceptible latency.” Access Control and public health What was once purely a security challenge—namely, how to prevent unauthorised entry into a restricted area—has evolved into a public health challenge as well. Many traditional access control methods, from number pads to fingerprint readers, require touch in order to function, and if the current global pandemic has made one thing evident, it’s that minimising physical contact between users and surfaces is vital to community well-being. Janus was originally designed to facilitate the next generation of facial-recognition-based access control readers—enabling 3D sensing and high recognition speed for seamless authentication. COVID-19 has accelerated industry-wide research, development, and timelines for Janus-based solutions, says Mr. Xu. Deep learning and artificial intelligence drive all the new capabilities offered in Janus—capabilities that are only possible due to the platform’s high computational horsepower. The core deep learning and AI capabilities of Janus enable a wide range of advanced features only possible with an embedded vision SoC, says Mr. Xu. All are performed in real time, even when multiple users are being processed simultaneously. These include the extraction and comparison of facial depth maps with those registered in the system; 3D liveness detection, ensuring that the system can distinguish between real users and photo or video playback attacks; anti-tailgating, which relies on computer vision algorithms to detect and track when an unauthorised person follows a legitimate user inside; face mask detection; and people counting. VCSEL technology According to Ken Huang, Director of Product Line Management, 3D Sensing, Lumentum: “Lumentum’s VCSEL technology is one of the Janus design’s core strengths and differentiators. The process begins when Lumentum’s high-resolution dot projector projects thousands of dots onto the scene to create a unique 3D depth pattern of a user’s face. Most traditional biometric facial security systems rely on 2D images of users—simple photographs—which reduces authentication accuracy. In contrast, the 3D depth map generated by Lumentum’s technology provides the foundation of a more accurate, more secure, and more intelligent system overall. In addition, Lumentum’s VCSEL solutions incorporate a Class 1, eye-safe laser with zero field failures to date.” Adds Paige Peng, Product Marketing Manager, Commercial Sensing Division, ON Semiconductor: “If we think of Ambarella’s CV25 as the brain of the Janus design, the AR0237IR from ON Semiconductor is the eye. The AR0237IR image sensor captures the information, and the CV25 processes it. Other face recognition systems use two “eyes” – one to recognise RGB patterns to generate the viewing image stream, and another IR module to detect liveliness in motion. The Janus solution leverages a single “eye”—the AR0237IR—to obtain both visible and infrared images for depth sensing and advanced algorithms such as anti-spoofing and 3D recognition. AR0237IR also provides good sensitivity in various lighting conditions and supports high-dynamic-range (HDR) functions.” The single-camera 3D sensing solution for access control operates in three seamless steps: Step 1: Lumentum’s high-resolution dot projector creates a unique 3D depth map of a user’s face; Step 2: ON Semiconductor’s RGB-IR image sensor captures the high-resolution images from Step 1, even in low-light or high dynamic range conditions; Step 3: Ambarella’s advanced vision SoC takes the high-resolution images captured in Step 2 and uses deep neural networks (DNNs) for depth processing, facial recognition, anti-tailgating, and anti-spoofing while video encoding and network software run simultaneously.
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