Quickly implement spoofing-resistant face recognition without a Cloud connection
Semiconductor’s MT9M114 image sensors. Here, one camera is equipped with a red, green, blue (RGB) filter, and the other camera is fitted with an infrared (IR) filter. Attached through camera interfaces to the vision application board, the RGB camera generates a normal visible light image, while the IR camera captures an image that would be different for a live person compared to an image of the person. Using this liveness detection approach along with its internal face recognition capability, the SLN-VIZNAS-IOT kit provides offline, anti-spoofing face recognition capability in a package measuring about 30 x 40 millimeters (mm) (Figure 5). Getting started with the SLN-VIZNAS-IOT kit The NXP SLN-VIZNAS-IOT kit comes ready-to-use with built- in face recognition models. Developers plug in a USB cable and touch a button on the kit to perform a simple manual face registration using the preloaded ‘elock’ application and the accompanying mobile app (Figure 6, left). After registration, the mobile app will display a ‘welcome home’ message and ‘unlocked’ label when the kit authenticates the registered face (Figure 6, right). The kit’s Oasis Lite face recognition software processes models from its database of up to 3000 RGB faces with a recognition accuracy
of 99.6%, and up to 100 IR faces with an anti-spoofing accuracy of 96.5%. As noted earlier, the NXP hardware/software solution needs less than one second (s) to perform face detection, image alignment, quality check, liveness detection, and recognition over a range from 0.2 to 1.0 meters (m). In fact, the system supports an alternate ‘light’ inference model capable of performing this same sequence in less than 0.5 s but supports a smaller maximum database size of 1000 RGB faces and 50 IR faces. Building custom face recognition applications Used as is, the NXP SLN-VIZNAS- IOT kit lets developers quickly evaluate, prototype and develop face recognition applications. When creating custom hardware solutions, the kit serves as a complete reference design with full schematics and a detailed bill of materials (BOM). For software development, programmers can use the NXP MCUXpresso integrated development environment (IDE) with FreeRTOS
Figure 5. The NXP SLN-VIZNAS- IOT hardware kit integrates a dual camera system for liveness detection (top) and a vision application board (bottom) with a connected module to provide a drop-in solution for offline face recognition with anti-spoofing capability. Image source: NXP
buck converter to supplement the i.MX RT106F processor’s internal power management capabilities for the additional devices on the connected module board. The connected module in turn mounts on a vision application board that combines a Murata Electronics IRA-S210ST01 passive infrared (PIR) sensor, motion sensor, battery charger, audio support, light emitting diodes (LEDs), buttons, and interface connectors (Figure 4). 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) Along with this system platform, a face recognition system design clearly requires a suitable camera sensor to capture an image of the user’s face. As mentioned earlier, however, concerns about presentation attacks require additional imaging capabilities.
Mitigating presentation attacks Researchers have for years explored different presentation attack detection (PAD) methods designed to mitigate attempts such as using latent fingerprints or images of a face to spoof biometric-based authentication systems. Although the details are well beyond the scope of this article, PAD methods in general use deep analysis of the quality and characteristics of the biometric data captured as part of the process, as well as ‘liveness’ detection methods designed to determine if the biometric data was captured from a live person. Underlying many of these different methods, deep neural network (DNN) models play an important role not only in face recognition, but also in identifying attempts to spoof the system. Nevertheless, the imaging system used to capture the user’s face can provide additional liveness detection support. For the SLN-VIZNAS-IOT kit, NXP includes camera modules that contain a pair of ON
Figure 7. NXP provides a comprehensive software environment that executes the NXP Oasis Lite runtime library and utility middleware on the FreeRTOS operating system. Image source: NXP
support and configuration tools. For this application, developers simply use NXP’s online MCUXpresso SDK Builder to configure their software development environment with NXP’s VIZNAS SDK, which includes the NXP Oasis Lite machine learning vision engine (Figure 7). The software package includes complete source code for the operating environment as well as the elock sample application mentioned earlier. NXP does not provide source code for its proprietary Oasis Lite engine or for the models. Instead, developers
work with the Oasis Lite runtime library using the provided application programming interface (API), which includes a set of intuitive function calls to perform supported operations. In addition, developers use a provided set of C defines and structures to specify various parameters including image size, memory allocation, callbacks and enabled functions used by the system when starting up the Oasis Lite runtime environment (Listing 1). The elock sample application code demonstrates the key design patterns for launching Oasis as a task running under FreeRTOS, initializing the environment and entering its normal run stage. In the run stage, the runtime environment operates on each frame of an image, executing the provided callback functions associated with each event defined in the environment (Listing 2).
Figure 6. The NXP SLN-VIZNAS-IOT hardware kit works out of the box, utilizing a companion app to register a face (left) and recognize registered faces (right). Image source: NXP
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