收稿日期: 2019-09-25
网络出版日期: 2019-12-25
基金资助
国家自然科学基金资助项目(61774100);上海市重大基础研发项目(18JC1410402)
Application of lightweight neural network and Hash tracking algorithm in embedded face capture system
Received date: 2019-09-25
Online published: 2019-12-25
针对嵌入式设备上难以兼顾人脸抓拍的速度和准确率的问题, 基于轻量化神经网络和哈希 (Hash) 跟踪算法设计了一种快速精准的嵌入式人脸抓拍系统. 首先, 对轻量化网络 MobileNet 固态硬盘 (solid state disk, SSD) 剪枝和优化网络结构构建人脸检测网络; 其次, 人脸对齐后基于均值哈希 (average Hash, aHash) 与感知哈希 (perceptual Hash, pHash) 设计融合哈希 (fusion Hash, fHash) 算法跟踪人脸, 使用关键点欧氏距离、人脸尺寸和四方向 Sobel 算子三标准提取最佳的人脸图像; 最后, 使用 MobileFaceNet 对最佳人脸进行识别. 实验结果表明: 与 MobileNet SSD 相比, 该人脸检测算法速度提升了 22.6%; 与均值哈希和感知哈希算法相比, 该融合哈希算法匹配准确率提高了 21.7% 和 10.1%; 实际场景中系统人脸抓拍准确率超过 95%, 抓拍速度达到 28 帧/s.
许庚林, 冉峰, 邓良, 史华康, 郭爱英 . 轻量化神经网络和哈希跟踪算法在嵌入式人脸抓拍系统中的应用[J]. 上海大学学报(自然科学版), 2021 , 27(6) : 1018 -1028 . DOI: 10.12066/j.issn.1007-2861.2187
To address the difficulty of balancing the speed and accuracy of face capture systems embedded in mobile devices, a fast and accurate embedded face capture system is designed based on the combination of a lightweight neural network and a Hash tracking algorithm. Firstly, an optimised face detection network is constructed based on the lightweight MobileNet solid state disk (SSD) object detector. After pruning and face alignment, a Hash algorithm is combined to track a face based on the average Hash (aHash) and a perceptual Hash (pHash) design, in which the key point distance and face size as well as the four-direction Sobel operator are used as the three standards to extract the best face image. Finally, MobileFaceNet is applied to identify the best face. The experimental results indicate that the speed of the proposed face detection algorithm is increased by 22.6% compared to using the standard MobileNet SSD. Furthermore, compared with using only the mean Hash or the pHash algorithm, the matching accuracy is improved by 21.7% and 10.1% when the combined Hash algorithm is applied, respectively. The face capture accuracy is greater than 95%, and the capture speed can reach 28 frame/s.
Key words： face capture; embedded; Hash tracking; deep learning
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