Paper
30 September 2024 Design of deep convolutional neural network cascade for face detection
Xiaoyuan Wang, Zanhui Fan, Wanxuan Yang, Mengfei Han
Author Affiliations +
Proceedings Volume 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024); 132860Z (2024) https://doi.org/10.1117/12.3045097
Event: Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 2024, Guangzhou, China
Abstract
This paper proposes to build a high performance and reconfigurable face detection acceleration system, and uses the method of hardware and software collaboration to give full play to the advantages of ARM and FPGA. It accelerates the MTCNN cascaded deep convolutional neural network framework while ensuring recognition accuracy, and ultimately completes a face detection system based on ZYNQ. This design is mainly divided into two parts: software and hardware. In terms of software, facial detection adopts the MTCNN cascaded deep convolutional neural network framework. It uses a carefully designed three-layer cascaded deep convolutional network to predict facial positions and facial keypoint coordinates from rough to fine. The MTCNN model is trained on the PC using the Caffe framework, OpenCV visual library, and C++ language, using the WIDER FACE dataset for training. Afterwards, the system will be written and deployed on the ZYNQ 7020 SOC platform using standard C language (C99 standard) on the Xilinx SDK software. The hardware part combines the ADV7511 controller and video buffer to achieve real-time image display, displaying the images stored in the SD card. Using ARM Cortex-A9 hardcore processor and VDMA as AXI slave device, direct memory access of video data is achieved.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xiaoyuan Wang, Zanhui Fan, Wanxuan Yang, and Mengfei Han "Design of deep convolutional neural network cascade for face detection", Proc. SPIE 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 132860Z (30 September 2024); https://doi.org/10.1117/12.3045097
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Facial recognition systems

Object detection

Windows

Design

Education and training

Convolutional neural networks

Image processing

Back to Top