iWave Systems i.MX8M Mini Board is a development platform based on an update versions of the company’s iW-RainboW-G34M-SM i.MX 8M Mini system-on-module and designed specifically for low-cost facial recognition systems thanks to NXP eIQ ML software, and MIPI display and camera.
iWave Systems i.MX8M Mini (aka iW-RainboW-G34D) specifications:
- SoC – NXP i.MX8M Mini Q/QL/D/DL/S/SL with up to 4x Cortex-A53 cores, 1x Cortex-M4F real-time core, Vivante 3D and 2D GPUs
- System Memory – 1GB LPDDR4 (Expandable up to 4GB)
- Storage – 8GB eMMC Flash (Expandable), optional 2MB QSPI Flash optional Micro SD slot
- Wireless – Dual-band 802.11a/b/g/n/ac WiFi 5 and Bluetooth 5.0.
- PMIC – BD71847AMWV
- i.MX8M SODIMM Carrier Board
- Storage – MicroSD slot
- Display I/F – MIPI DSI display connector
- Camera I/F – MIPI CSI camera connector
- Audio – Audio codec, 3.5mm Line In/Out jack
- Networking – Up to 2x Gigabit Ethernet ports (One is Optional)
- USB – 2x USB 2.0 Host ports, 1x USB 2.0 device port
- Debugging – Serial console on Micro USB Port, JTAG header
- Expansion – 1x Data UART, GPIO Header, eCSPI (Enhanced Configurable Serial Peripheral Interface) header
- Misc – RTC Coin cell, boot mode switch; ON/OFF, reset Switch
- Dimensions – 100 x 72 mm (Pico-ITX form factor)
- Display – 5.5” HD AMOLED MIPI DSI display
- Camera – OV5640 camera module up to 1080p30
- Power Supply – 5V @ 1A DC Input
- Temperature Range – 0°C to +60°C
- Compliance – REACH & RoHS
The board supports Linux 4.14.98, and Android 9.0. NXP eIQ machine learning software is based on OpenCV and includes pre-optimized libraries and tools for computer vision applications. C++, Python, and Java API, and integration with MCUXpresso SDK and Yocto development environments aims to accelerate the development flow of the ML applications.
A facial recognition demo is included with the development kit. It relies on a Qt5 GUI, eIQ OpenCV ML software to compare the faces captured by the camera against registered face images in a database using the Django framework. The board also sends real-time data to the cloud via Ethernet and WiFi, so that authorized access and unauthorized detection can be visualized on a web dashboard.
One of the main use cases of the development kit is access control with face recognition solutions being much safer than fingerprint-based access control solutions in the time of COVID-19, as it enables zero-contact access application by using individuals’ faces to authorize access to a commercial/industrial space, home/office, transportation, banking, and government sites.