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基于卷积神经网络的教室人脸检测算法

Classroom Face Detection Algorithm Based on Convolutional Neural Network

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摘要

针对教室场景下后排学生人脸微小难以检测的情况,提出一种基于卷积神经网络的教室人脸检测算法。采用两阶段检测形式,运用残差神经网络的结构对教室人脸进行特征提取,同时构建特征金字塔,并将Softmax损失函数与中心特征损失函数结合,运用合适的激活函数进行训练。此算法在教室场景下获得95.2%的准确率,且在通用数据集Wider Face的三个等级验证集上分别获得93.0%,87.3%,58.3%的平均精度均值。

Abstract

This study proposes a face detection algorithm based on a convolutional neural network considering the scenario of a classroom, where the faces of students sitting in the back rows might not be visible. First, the algorithm extracts face features in two stages using a residual neural network. Then, it builds a feature pyramid and combines the Softmax loss function with center loss function to train a face recognition model based on a proper activation function. Upon applying the algorithm to the Wider Face dataset, it achieves an accuracy of 95.2% and mean average precision values of 93.0%, 87.3%, and 58.3% for three levels of validation sets, respectively.

Newport宣传-MKS新实验室计划
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中图分类号:TP391

DOI:10.3788/LOP56.211501

所属栏目:机器视觉

基金项目:广州市科技计划项目;

收稿日期:2019-03-28

修改稿日期:2019-04-26

网络出版日期:2019-11-01

作者单位    点击查看

王萌:天津大学电气自动化与信息工程学院, 天津 300072
苏寒松:天津大学电气自动化与信息工程学院, 天津 300072
刘高华:天津大学电气自动化与信息工程学院, 天津 300072
李燊:天津大学电气自动化与信息工程学院, 天津 300072

联系人作者:刘高华(suppig@126.com)

备注:广州市科技计划项目;

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引用该论文

Wang Meng,Su Hansong,Liu Gaohua,Li Shen. Classroom Face Detection Algorithm Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211501

王萌,苏寒松,刘高华,李燊. 基于卷积神经网络的教室人脸检测算法[J]. 激光与光电子学进展, 2019, 56(21): 211501

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