Quickly implement spoofing-resistant face recognition without a Cloud connection
of authentication: “Something you know”, such as a password; “Something you have”, such as a physical key or key card; and “Something you are”, which is typically a biometric factor such as a fingerprint or iris. Using this approach, a strongly authenticated door lock might require the user to enter a passcode, use a key card, and further provide a fingerprint to unlock the door. In practice, such stringent requirements are bothersome or simply impractical for consumers who need to frequently and easily re- authenticate themselves with a smartphone or other routinely used device. The use of face recognition has significantly simplified authentication for smartphone users, but smartphones possess some advantages that might not be available in every device. Besides the significant processing power available in leading- edge smartphones, always-on connectivity is a fundamental requirement for delivering the sophisticated range of services routinely expected by their users. For many products that require secure authentication, the underlying operating platform will typically provide more modest computing resources and more limited connectivity. Face recognition services from the leading cloud-service providers shift the processing load to the cloud, but the need for robust
connectivity to ensure minimal response latency might impose requirements that remain beyond the capabilities of the platform. Of equal or more concern to users, transmitting their image across public networks for processing and potentially storing it in the cloud raises significant privacy issues. Using NXP Semiconductors’ i.MX RT106F processors and associated software, developers can now implement offline face recognition that directly addresses these concerns. Hardware and software for spoof-proof offline face recognition A member of the NXP i.MX RT1060 Crossover microcontroller (MCU) family, the NXP i.MX RT106F series is specifically designed to support easy integration of offline face recognition into smart home devices, consumer appliances, security devices, and industrial equipment. Based on an Arm Cortex-M7 processor core, the processors run at 528 megahertz (MHz) for the industrial grade MIMXRT106FCVL5B , or 600MHz for commercial grade processors such as the MIMXRT106FDVL6A and MIMXRT106FDVL6B . Besides supporting a wide range of external memory interfaces, i.MX RT106F processors include 1 megabyte (Mbyte) of on-chip random access memory (RAM) with 512 kilobytes (Kbyte)
Face recognition has gained widespread acceptance for authenticating access to
smartphones but attempts to apply this technology more broadly have fallen short in other areas despite its effectiveness and ease of use. Along with the technical challenges of implementing reliable, low- cost machine learning solutions, developers must address user concerns around the reliability and privacy of conventional face recognition methods that depend on cloud connections that are vulnerable to spoofing. This article discusses the difficulty of secure authentication before
Written by: Stephen Evanczuk
introducing a hardware and software solution from NXP
Semiconductors that addresses the issues. It then shows how developers without prior experience in machine learning methods can use the solution to rapidly implement offline anti-spoofing face recognition in a smart product. The challenges of secure authentication for smart products In addressing growing concerns about the security of smart products, developers have found themselves left with few tenable options for reliably authenticating users looking for quick yet secure access. Traditional methods rely on multifactor authentication methods that rest on some combination of the classical three factors
we get technical
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