Chaos LiDAR Based RGB-D Face Classification System With Embedded CNN Accelerator on FPGAs

Face classification is important in many applications such as surveillance, border control, and security systems. However, wide variations in environments such as insufficient light, large distances or pose angles make the task challenging. Depth sensors are added with RGB cameras for improving classification accuracy but commercial RGB-D sensors are most targeted for indoors applications. In this paper, we present and design a Chaos LiDAR depth sesnor that provides high-precision depth images through intelligent correlation processing for both indoors and outdoors applications. Our Chaos LiDAR depth sensor detects range from 2 to 40 meters with precision around 8mm at 20-meter. With the Chaos LiDAR depth as input, we design a RGB-D based face classification embedded CNN (eCNN) model for wide range applications such as dim illumination, various distances and large poses. Our Chaos LiDAR increases around 14.27% classification accuracy compared to RealSense D435i for distance from 3 to 5 meter. The eCNN face classification subsystem is implemented in Xilinx ZCU 102 and achieves 11.11 ms inference time. The eCNN engine achieves a peak throughput at 614.4 GOPS. The overall system including Chaos LiDAR, correlation and eCNN FPGA achieves face classification inference rate of 10fps.

Automated summary

Face classification is important in various applications, but is challenging due to environmental variations. In this paper, we propose a Chaos LiDAR depth sensor for both indoor and outdoor applications, and design a face classification model based on RGB-D sensing. Experimental results show that our Chaos LiDAR sensor achieves higher classification accuracy compared to traditional sensors within a certain distance range.