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基于跨连接LeNet-5网络的面部表情识别

李勇 林小竹 蒋梦莹

李勇, 林小竹, 蒋梦莹. 基于跨连接LeNet-5网络的面部表情识别. 自动化学报, 2018, 44(1): 176-182. doi: 10.16383/j.aas.2018.c160835
引用本文: 李勇, 林小竹, 蒋梦莹. 基于跨连接LeNet-5网络的面部表情识别. 自动化学报, 2018, 44(1): 176-182. doi: 10.16383/j.aas.2018.c160835
LI Yong, LIN Xiao-Zhu, JIANG Meng-Ying. Facial Expression Recognition with Cross-connect LeNet-5 Network. ACTA AUTOMATICA SINICA, 2018, 44(1): 176-182. doi: 10.16383/j.aas.2018.c160835
Citation: LI Yong, LIN Xiao-Zhu, JIANG Meng-Ying. Facial Expression Recognition with Cross-connect LeNet-5 Network. ACTA AUTOMATICA SINICA, 2018, 44(1): 176-182. doi: 10.16383/j.aas.2018.c160835

基于跨连接LeNet-5网络的面部表情识别

doi: 10.16383/j.aas.2018.c160835
基金项目: 

国家自然科学基金 60772168

详细信息
    作者简介:

    李勇 北京化工大学硕士研究生.主要研究方向为图像处理与模式识别, 深度学习.E-mail:15117965051@163.com

    蒋梦莹 北京化工大学硕士研究生.主要研究方向为图像处理与模式识别, 深度学习.E-mail:18810493772@163.com

    通讯作者:

    林小竹 北京石油化工学院教授.主要研究方向为图像处理与模式识别, 深度学习, 信号与系统.本文通信作者.E-mail:linzhu1964@163.com

Facial Expression Recognition with Cross-connect LeNet-5 Network

Funds: 

National Natural Science Foundation of China 60772168

More Information
    Author Bio:

    Master student at the College of Information Science and Technology, Beijing University of Chemical Technology. His research interest covers image processing, pattern recognition, and deep learning

    Master student at the College of Information Science and Technology, Beijing University of Chemical Technology. Her research interest covers image processing, pattern recognition, and deep learning

    Corresponding author: LIN Xiao-Zhu Professor at the School of Information Engineering, Beijing Institute of Petrochemical Technology. His research interest covers image processing and pattern recognition, deep learning, and signals and systems. Corresponding author of this paper
  • 摘要: 为避免人为因素对表情特征提取产生的影响,本文选择卷积神经网络进行人脸表情识别的研究.相较于传统的表情识别方法需要进行复杂的人工特征提取,卷积神经网络可以省略人为提取特征的过程.经典的LeNet-5卷积神经网络在手写数字库上取得了很好的识别效果,但在表情识别中识别率不高.本文提出了一种改进的LeNet-5卷积神经网络来进行面部表情识别,将网络结构中提取的低层次特征与高层次特征相结合构造分类器,该方法在JAFFE表情公开库和CK+数据库上取得了较好的结果.
    1)  本文责任编委 胡清华
  • 图  1  LeNet-5结构图

    Fig.  1  The LeNet-5 convolutional neural network

    图  2  改进的LeNet-5卷积神经网络

    Fig.  2  Improved LeNet-5 convolutional neural network

    图  3  JAFFE表情库7种表情示例图像

    Fig.  3  7 kinds of facial expression image in JAFFE expression dataset

    图  4  CK+表情库7种表情示例图像

    Fig.  4  7 kinds of facial expression image in the CK+ expression dataset

    表  1  LeNet-5网络Layer 2与Layer 3之间的连接方式

    Table  1  Connection between LeNet-5 network0s Layer 2 and Layer 3

    12345678910111213141516
    1
    2
    3
    4
    5
    6
    下载: 导出CSV

    表  2  卷积网络参数

    Table  2  Convolutional network parameters

    输入输入尺寸卷积核大小池化区域步长输出尺寸
    Input32 × 325 × 5128 × 28
    Layer 16 @ 28 × 282 × 226@14 × 14
    Layer 26 @ 14 × 145 × 5110 × 10
    Layer 316 @ 10 × 102 × 2216 @ 5 × 5
    Layer 416 @ 5 × 55 × 51120@1 × 1
    Layer 5120 @ 1 × 11 × 84
    Layer 61 × 1 6601 × 7
    Output1 × 7
    下载: 导出CSV

    表  3  JAFFE表情库不同表情的分类正确率(%)

    Table  3  Classification accuracy of different expressions in JAFFE expression dataset (%)

    生气厌恶害怕高兴中性悲伤惊讶整体
    测试集11008010010010090.9188.8994.37
    测试集2100909081.8210010010092.96
    测试集310010081.8290.9110010010095.77
    整体10089.6690.6390.6310096.7796.5594.37
    下载: 导出CSV

    表  4  CK+数据库不同表情的分类正确率(%)

    Table  4  Classification accuracy of different expressions in CK+ dataset (%)

    生气厌恶害怕高兴中性悲伤惊讶整体
    测试集188.8994.448092.8670.839693.9488.89
    测试集270.3777.788096.30688496.9782.32
    测试集377.7885.7184.62100647293.9483.33
    测试集462.9694.298889.29608087.8880.81
    测试集581.4885.717292.866479.1710083.33
    整体76.3087.5980.9294.2665.3782.2394.5583.74
    下载: 导出CSV

    表  5  网络是否跨连接正确率对比(%)

    Table  5  Classification accuracy of the network whether cross connection or not (%)

    方法参数量JAFFE表情库中平均正确率CK+数据库中平均正确率
    LeNet-514 44462.4432.32
    本文方法25 47694.3783.74
    下载: 导出CSV

    表  6  不同方法在JAFFE上的对比(%)

    Table  6  The comparison of different methods on JAFFE (%)

    来源方法正确率
    Kumbhar等[28]*Image feature60 ~ 70
    Praseeda等[5]*SVM86.9
    本文算法跨连的LeNet-594.37
      *数据来源于文献[15]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-12-23
  • 录用日期:  2017-05-04
  • 刊出日期:  2018-01-20

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