EdgeReady crossover processors for 3D facial recognition

Mouser now stocks the i.MX RT117F crossover processors from NXP Semiconductors. They expand on the company's EdgeReady portfolio, the crossover processors provide a low-cost, embedded, secure 3D facial recognition solution. This innovative solution allows developers of smart locks and other access control systems to add machine learning-based secure facial recognition abilities quickly and efficiently to smart homes and smart IIoT-based applications using its eIQ machine learning software.

The crossover processors feature the company's advanced implementation of the Arm Cortex-M7 CPU core with 2MB of on-chip SRAM, running at up to 1GHz to offer higher performance, faster, more accurate facial recognition, enhanced power efficiency and an overall better user experience. Along with its dual-core processing and security features, the device also offers a 2D GPU, two Gigabit Ethernet ports (and one 10/100 port), two High-Speed USB OTG with PHY, and support for a MIPI display and camera. The combined solution of the crossover processor with a high-performance 3D structured light module camera helps provide the performance and security of 3D facial recognition at the edge, addressing privacy concerns and eliminating latency typically associated with cloud-based alternatives.

The device also includes a license to run its facial biometric authentication library. The comprehensive library hosts a robust providing of camera drivers, image capture and pre-processing tools, alignment and recognition tools, wireless drivers, face detection, tracking, automation scripts and more.

Mouser also stocks the SLN-VIZN3D-IOT development kit to complement the crossover processors for development. The kit implements an onboard i.MX RT117F processor for secure facial recognition with 3D liveness detection and provides fully integrated turnkey software for quick out-of-the-box operation. Also, the kit contains an available remote registration capability to permit end-users to register their faces from mobile devices.