Quickly implement spoofing-resistant face recognition without a Cloud connection
additional devices to provide a complete hardware platform (Figure 3). The kit’s connected module board combines an NXP MIMXRT106FDVL6A i.MX RT106F processor, an NXP A71CH secure element, and two connectivity options – NXP’s MKW41Z512VHT4 Kinetis KW41Z Bluetooth low energy (BLE) system-on-chip (SoC) and Murata Electronics’ LBEE5KL1DX-883 Wi-Fi/Bluetooth module. To supplement the processor’s on-chip memory, the connected module adds Winbond Electronics’ W9825G6JB 256 megabit (Mbit) synchronous dynamic RAM (SDRAM), an Integrated Silicon Solution. Inc . (ISSI) IS26KL256S- DABLI00 256 Mbit NOR flash, and ISSI’s IS25LP256D 256 Mbit Quad Serial Peripheral Interface (SPI) device. Finally, the module adds a Torex Semiconductor XCL214B333DR
Microcontroller System Interface Standard NN (CMSIS-NN) library (Figure 2). The inference models reside on the i.MX RT106F platform, so face detection and recognition execute locally, unlike other solutions that depend on Cloud-based resources to run the machine learning algorithms. Thanks to this offline face recognition capability, designers of smart products can ensure private, secure authentication despite low bandwidth or spotty Internet connectivity. Furthermore, authentication occurs quickly with this hardware and software combination, requiring less than 800 milliseconds (ms) for the processor to wake from low- power standby and complete face recognition. Used with the i.MX RT106F processor, the Oasis Lite runtime simplifies implementation of offline face recognition for smart products, but the processor
Figure 3. The NXP SLN-VIZNAS-IOT kit includes a connected module that provides a robust connected system platform needed to run authentication software. Image source: NXP
and runtime environment are of course only part of a required system solution. Along with a more complete set of system components, an effective authentication solution requires imaging capability that can mitigate a type of security threat called presentation attacks. These attacks attempt to spoof face recognition authentication by using photographs. For developers looking to rapidly deploy face- based authentication in their own products, the NXP SLN-VIZNAS-IOT development kit and associated software provide a ready-to-use platform for evaluation, prototyping and development of offline, anti- spoofing face recognition.
Complete secure systems solution for face recognition As with most advanced processors, the i.MX RT106F processor requires only a few additional components to provide an effective computing platform. The NXP SLN-VIZNAS- IOT kit completes the design by integrating the i.MX RT106F with
Figure 1. NXP Semiconductor’s i.MX RT106F processors combine a full set of functional blocks needed to support face recognition for consumer, industrial and security products. Image source: NXP
configured as general purpose RAM, and 512 Kbytes that can be configured either as general purpose RAM or as tightly coupled memory (TCM) for instructions (I-TCM) or data (D-TCM). Along with on-chip power management, these processors offer an extensive set of integrated features for graphics, security, system control, and both analog and digital interfaces typically needed to support consumer devices, industrial human machine interfaces (HMIs), and motor control (Figure 1). Although similar to other i.MX RT1060 family members, i.MX RT106F processors bundle in a runtime license for NXP’s Oasis
Lite face recognition software. Designed to speed inference on this class of processors, the Oasis Lite runtime environment performs face detection, recognition, and even limited emotion classification using
neural network (NN) inference models running on an inference engine and MiniCV – a stripped- down version of the open source OpenCV computer vision library. The inference engine builds on an NXP
Figure 2. The NXP Oasis Lite runtime library includes an Oasis Lite core that uses MiniCV and an NXP inference engine built on neural network libraries from NXP and Arm. Image source: NXP
Figure 4. In the NXP SLN-VIZNAS-IOT kit, the connected module (left) is attached to the vision application board to provide the hardware foundation for face recognition. Image source: NXP
NN library and the Arm Cortex
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