Research and Implementation of a Facial Emotion Recognition System Based on ZYNQ - Jiang, Jiali (International Journal of Computer Science and Information Technology, 2024)
Emotion is the psychological and physiological response of a person or animal to a specific stimulus or event, and it can also have an impact on cognition, decision-making, and behavior. Research on emotion recognition has wide applications in various fields, such as dialogue and communication with machines, and in the medical field. At present, most emotion recognition systems are based on PC or embedded platforms. Compared with traditional emotion recognition platforms, FPGA has good parallel computing ability and can process multiple data streams simultaneously, making it suitable for complex pattern recognition in emotion recognition. This article chooses ZYNQ as the development platform, combined with the collaborative ability of FPGA and ARM processors, to build a complete hardware application system that can carry neural networks. Secondly, in response to the problems of complex computation and high storage resource consumption in existing neural network computing frameworks, this paper adopts a lightweight neural network MobileNetV3 and implements and improves it. The model is encapsulated and ported using the Vivado HLS development tool and C++. The hardware IP core is called on the hardware platform to achieve ZYNQ based facial emotion classification and recognition, and the power consumption of hardware resources is analyzed.