| 基于深度学习的ARM平台实时人脸识别 |
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| 引用本文： | 方国康,李俊,王垚儒.基于深度学习的ARM平台实时人脸识别[J].计算机应用,2019,39(8):2217-2222. |
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| 作者姓名： | 方国康 李俊 王垚儒 |
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| 作者单位： | 武汉科技大学计算机科学与技术学院,武汉430065;智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学),武汉430065;武汉科技大学计算机科学与技术学院,武汉430065;智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学),武汉430065;武汉科技大学计算机科学与技术学院,武汉430065;智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学),武汉430065 |
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| 基金项目： | 国家自然科学基金资助项目（61572381）；武汉科技大学智能信息处理与实时工业系统湖北重点实验室基金资助项目（znxx2018QN06）。 |
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| 摘 要： | 针对ARM平台上人脸识别实时性不强和识别率低的问题,提出一种基于深度学习的实时人脸识别方法。首先基于MTCNN人脸检测算法设计了一种实时检测并追踪人脸的算法;然后在ARM平台上基于深度残差网络(ResNet)设计人脸特征提取网络;最后针对ARM平台的特点,使用Mali-GPU加速人脸特征提取网络的运算,分担CPU负荷,提高系统整体运行效率。算法部署在基于ARM的瑞芯微RK3399开发板上,运行速度达到22帧/s。实验结果表明,与MobileFaceNet相比,该方法在MegaFace上的识别率提升了11个百分点。
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| 关 键 词： | ARM平台 人脸识别 人脸追踪 残差网络 Mali-GPU |
| 收稿时间： | 2019-01-22 |
| 修稿时间： | 2019-03-14 |
Real-time face recognition on ARM platform based on deep learning |
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| FANG Guokang,LI Jun,WANG Yaoru.Real-time face recognition on ARM platform based on deep learning[J].journal of Computer Applications,2019,39(8):2217-2222. |
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| Authors: | FANG Guokang LI Jun WANG Yaoru |
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| Affiliation: | 1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System(Wuhan University of Science and Technology), Wuhan Hubei 430065, China |
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| Abstract: | Aiming at the problem of low real-time performance of face recognition and low face recognition rate on ARM platform, a real-time face recognition method based on deep learning was proposed. Firstly, an algorithm for detecting and tracking faces in real time was designed based on MTCNN face detection algorithm. Then, a face feature extraction network was designed based on Residual Neural Network (ResNet) on ARM platform. Finally, according to the characteristics of ARM platform, Mali-GPU was used to accelerate the operation of face feature extraction network, sharing the CPU load and improving the overall running efficiency of the system. The algorithm was deployed on ARM-based Rockchip development board, and the running speed reaches 22 frames per second. Experimental results show that the recognition rate of this method is 11 percentage points higher than that of MobileFaceNet on MegaFace. |
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| Keywords: | ARM platform face recognition face tracking Residual neural Network (ResNet) Mali-GPU |
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