Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1253-1259.DOI: 10.11772/j.issn.1001-9081.2021071270
Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)
• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles
Guifang QIAO1, Shouming HOU1(), Yanyan LIU2
Received:
2021-07-06
Revised:
2021-08-22
Accepted:
2021-08-31
Online:
2022-04-15
Published:
2022-04-10
Contact:
Shouming HOU
About author:
QIAO Guifang, born in 1995, M. S. candidate. Her research interests include graphics and image processing.Supported by:
通讯作者:
侯守明
作者简介:
乔桂芳(1995—),女,河南开封人,硕士研究生,主要研究方向:图形图像处理基金资助:
CLC Number:
Guifang QIAO, Shouming HOU, Yanyan LIU. Facial expression recognition algorithm based on combination of improved convolutional neural network and support vector machine[J]. Journal of Computer Applications, 2022, 42(4): 1253-1259.
乔桂芳, 侯守明, 刘彦彦. 基于改进卷积神经网络与支持向量机结合的面部表情识别算法[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1253-1259.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021071270
模型结构 | 层类型 | 输入尺寸 | 卷积核 | 步长 | 填充 | 输出尺寸 |
---|---|---|---|---|---|---|
Input | 48×48×1 | 48×48×1 | ||||
ConvBlock_1 | Convolution1_1 | 48×48×1 | 3×3 | 1 | Same | 48×48×32 |
Convolution1_2 | 48×48×32 | 3×3 | 1 | Same | 48×48×32 | |
Max pooling | 48×48×32 | 2×2 | 2 | 24×24×32 | ||
ConvBlock_2 | Convolution2_1 | 24×24×32 | 3×3 | 1 | Same | 24×24×64 |
Convolution2_2 | 24×24×64 | 3×3 | 1 | Same | 24×24×64 | |
Maxpooling | 24×24×64 | 2×2 | 2 | 12×12×64 | ||
ConvBlock_3 | Convolution3_1 | 12×12×64 | 3×3 | 1 | Same | 12×12×128 |
Convolution3_2 | 12×12×128 | 3×3 | 1 | Same | 12×12×128 | |
Maxpooling | 12×12×128 | 2×2 | 2 | 6×6×128 | ||
全局平均池化 | GAP | 6×6×128 | 1×1×128 | |||
分类判别 | SVM | 1×1×128 | 1×7 |
Tab. 1 Parameter description of each layer of facial expression recognition model based on improved CNN+SVM algorithm
模型结构 | 层类型 | 输入尺寸 | 卷积核 | 步长 | 填充 | 输出尺寸 |
---|---|---|---|---|---|---|
Input | 48×48×1 | 48×48×1 | ||||
ConvBlock_1 | Convolution1_1 | 48×48×1 | 3×3 | 1 | Same | 48×48×32 |
Convolution1_2 | 48×48×32 | 3×3 | 1 | Same | 48×48×32 | |
Max pooling | 48×48×32 | 2×2 | 2 | 24×24×32 | ||
ConvBlock_2 | Convolution2_1 | 24×24×32 | 3×3 | 1 | Same | 24×24×64 |
Convolution2_2 | 24×24×64 | 3×3 | 1 | Same | 24×24×64 | |
Maxpooling | 24×24×64 | 2×2 | 2 | 12×12×64 | ||
ConvBlock_3 | Convolution3_1 | 12×12×64 | 3×3 | 1 | Same | 12×12×128 |
Convolution3_2 | 12×12×128 | 3×3 | 1 | Same | 12×12×128 | |
Maxpooling | 12×12×128 | 2×2 | 2 | 6×6×128 | ||
全局平均池化 | GAP | 6×6×128 | 1×1×128 | |||
分类判别 | SVM | 1×1×128 | 1×7 |
angry | 生气 | 3 196 | 799 | |
disgust | 厌恶 | 348 | 88 | |
fear | 恐惧 | 3 277 | 820 | |
happy | 高兴 | 5 772 | 1 443 | |
sad | 悲伤 | 3 864 | 966 | |
surprised | 惊讶 | 2 536 | 635 | |
normal | 中性 | 3 972 | 993 |
Tab. 2 Chinese and English labels and numbers of different categories in Fer2013 dataset
angry | 生气 | 3 196 | 799 | |
disgust | 厌恶 | 348 | 88 | |
fear | 恐惧 | 3 277 | 820 | |
happy | 高兴 | 5 772 | 1 443 | |
sad | 悲伤 | 3 864 | 966 | |
surprised | 惊讶 | 2 536 | 635 | |
normal | 中性 | 3 972 | 993 |
表情类别 | |||
---|---|---|---|
训练集 | 测试集 | 总数量 | |
愤怒 | 108 | 27 | 135 |
蔑视 | 42 | 12 | 54 |
厌恶 | 142 | 35 | 177 |
恐惧 | 60 | 15 | 75 |
高兴 | 166 | 41 | 207 |
悲伤 | 67 | 17 | 84 |
惊讶 | 199 | 50 | 249 |
Tab. 3 Number of each expression category in CK+ dataset
表情类别 | |||
---|---|---|---|
训练集 | 测试集 | 总数量 | |
愤怒 | 108 | 27 | 135 |
蔑视 | 42 | 12 | 54 |
厌恶 | 142 | 35 | 177 |
恐惧 | 60 | 15 | 75 |
高兴 | 166 | 41 | 207 |
悲伤 | 67 | 17 | 84 |
惊讶 | 199 | 50 | 249 |
参数 | 值 |
---|---|
批量(Batch_size) | 24 |
迭代次数(Epochs) | 200 |
随机失活(Dropout) | 0.2 |
学习率(Lr) | 0.001 |
表情类别(Nc) | 7 |
Tab. 4 Model training parameter description
参数 | 值 |
---|---|
批量(Batch_size) | 24 |
迭代次数(Epochs) | 200 |
随机失活(Dropout) | 0.2 |
学习率(Lr) | 0.001 |
表情类别(Nc) | 7 |
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