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基于跨连卷积神经网络的性别分类模型

张婷 李玉鑑 胡海鹤 张亚红

张婷, 李玉鑑, 胡海鹤, 张亚红. 基于跨连卷积神经网络的性别分类模型. 自动化学报, 2016, 42(6): 858-865. doi: 10.16383/j.aas.2016.c150658
引用本文: 张婷, 李玉鑑, 胡海鹤, 张亚红. 基于跨连卷积神经网络的性别分类模型. 自动化学报, 2016, 42(6): 858-865. doi: 10.16383/j.aas.2016.c150658
ZHANG Ting, LI Yu-Jian, HU Hai-He, ZHANG Ya-Hong. A Gender Classification Model Based on Cross-connected Convolutional Neural Networks. ACTA AUTOMATICA SINICA, 2016, 42(6): 858-865. doi: 10.16383/j.aas.2016.c150658
Citation: ZHANG Ting, LI Yu-Jian, HU Hai-He, ZHANG Ya-Hong. A Gender Classification Model Based on Cross-connected Convolutional Neural Networks. ACTA AUTOMATICA SINICA, 2016, 42(6): 858-865. doi: 10.16383/j.aas.2016.c150658

基于跨连卷积神经网络的性别分类模型

doi: 10.16383/j.aas.2016.c150658
基金项目: 

国家自然科学基金 61175004

高等学校博士学科点专项科研基金 20121103110029

北京市博士后工作资助项目 2015ZZ-24: Q6007011201501

详细信息
    作者简介:

    李玉鑑 北京工业大学计算机学院教授. 主要研究方向为模式识别, 图像处理, 机器学习, 数据挖掘. E-mail: liyujian@bjut.edu.cn

    胡海鹤 北京工业大学计算机学院博士后. 主要研究方向为模式识别, 机器学习, 红外技术. E-mail: huhaihe@bjut.edu.cn

    张亚红 北京工业大学计算机学院博士研究生. 主要研究方向为模式识别, 数据挖掘, 大数据分析. E-mail: plahpu@163.com

    通讯作者:

    张婷 北京工业大学计算机学院博士研究生. 主要研究方向为模式识别, 深度学习, 大数据分析. 本文通信作者. E-mail: zhangting08@emails.bjut.edu.cn

A Gender Classification Model Based on Cross-connected Convolutional Neural Networks

Funds: 

National Natural Science Foundation of China 61175004

Specialized Research Fund for the Doctoral Program of Higher Education of China 20121103110029

Project Funding of Postdoctor in Beijing 2015ZZ-24: Q6007011201501

More Information
    Author Bio:

    LI Yu-Jian Professor at the Com-puter School, Beijing University of Technology. His research interest cov-ers pattern recognition, image process-ing, machine learning, and data mining

    HU Hai-He Postdoctor at the Computer School, Beijing University of Technology. Her research interest cov-ers pattern recognition, machine learn-ing, and infrared technology

    ZHANG Ya-Hong Ph. D. candi-date at the Computer School, Bei-jing University of Technology. Her re-search interest covers pattern recogni-tion, data mining, and big data analysis

    Corresponding author: ZHANG Ting Ph. D. candidate at the Computer School, Beijing Univer-sity of Technology. Her research inter-est covers pattern recognition, deep learning, and big data analysis. Corresponding author of this paper
  • 摘要: 为提高性别分类准确率, 在传统卷积神经网络(Convolutional neural network, CNN)的基础上, 提出一个跨连卷积神经网络(Cross-connected CNN, CCNN)模型. 该模型是一个9层的网络结构, 包含输入层、6个由卷积层和池化层交错构成的隐含层、全连接层和输出层, 其中允许第2个池化层跨过两个层直接与全连接层相连接. 在10个人脸数据集上的性别分类实验结果表明, 跨连卷积网络的准确率均不低于传统卷积网络.
  • 图  1  跨连卷积神经网络结构示意图

    Fig.  1  The crossed-connected convolutional neural network

    图  2  10 个数据集中的示例人脸图像

    Fig.  2  Examples of face images in ten datasets

    表  1  CCNN 的网络描述

    Table  1  Description of the CCNN

    Layer Type Patch size Stride Output size
    x Input 32×32
    h1 Convolution 5×5 1 28×28×6
    h2 Mean pooling 2×2 214×14×6
    h3 Convolution 5×5 110×10×12
    h4 Mean pooling 2×2 25×5×12
    h5 Convolution 2×2 14×4×16
    h6 Mean pooling 2×2 22×2×16
    h7 Fully-connected 364
    o Output 2
    下载: 导出CSV

    表  2  实验数据集的训练集和测试集信息描述

    Table  2  Number of training samples and testing samples of the experiments

    数据集 训练集 测试集
    混合 混合
    UMIST 209 57 266 95 19 114
    ORL 320 30 350 40 10 50
    Georgia Tech 4507552519530225
    FERET 6585321190105105
    Extended Yale B 1 280 3841 664 576192768
    AR 9109101 820 390390780
    Faces94 2 000 4002 400 66020680
    LFW 8 000 1 900 9 900 2 000 8002 800
    MORPH 40 997 7 102 48 099 3 000 1 000 4 000
    CelebFaces+ 27 887 37 113 65 000 2 500 2 500 5 000
    下载: 导出CSV

    表  3  CNN 和CCNN 在10 个数据集上的分类准确率(%)

    Table  3  Classi¯cation accuracies of CNN and CCNN in ten datasets (%)

    数据集CNN CCNN
    UMIST 96.4999.20
    ORL 9898.00
    Georgia Tech 97.697.78
    FERET 94.7796.44
    Extended Yale B 98.5398.82
    AR 98.7198.71
    Faces94 96.4697.35
    LFW 8787.86
    MORPH 92.7394.56
    CelebFaces+ 85.1888.70
    下载: 导出CSV

    表  4  CNN 和CCNN 在4 个数据集上的分类准确率(%)

    Table  4  Classi¯cation accuracies of CNN and CCNN in four datasets (%)

    数据集 CNN CCNN
    混合 混合
    Georgia Tech 99.49 95.71 97.6 99.49 96.07 97.78
    Extended Yale B 100 97.06 98.53 100 97.64 98.82
    Faces94 98.4894.4496.4610094.797.35
    LFW 95 79 87 96.8 78.92 87.86
    下载: 导出CSV

    表  5  CCNN 在不同跨连方式的分类准确率(%)

    Table  5  Classif cation accuracies of the CCNN withdifferent cross-connections (%)

    数据集 h2-h7 h3-h7 h4-h7 h5-h7
    Georgia Tech 97.9697.8497.7897.33
    AR 98.8598.8598.7198.59
    Faces94 97.597.3597.3597.35
    LFW 88.1388.0487.8687.86
    MORPH 94.6394.6394.5694.45
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-10-16
  • 录用日期:  2016-04-01
  • 刊出日期:  2016-06-20

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