[1]吕 鹏,单剑锋.基于多特征融合的人脸表情识别算法[J].计算机技术与发展,2022,32(10):151-155.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 025]
　LYU Peng,SHAN Jian-feng.Facial Expression Recognition Algorithm Based on Multi-feature Fusion[J].,2022,32(10):151-155.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 025]
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基于多特征融合的人脸表情识别算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年10期
页码:
151-155
栏目:
人工智能
出版日期:
2022-10-10

文章信息/Info

Title:
Facial Expression Recognition Algorithm Based on Multi-feature Fusion
文章编号:
1673-629X(2022)10-0151-05
作者:
吕 鹏; 单剑锋
南京邮电大学 电子信息与光学工程、微电子学院,江苏 南京 210023
Author(s):
LYU Peng; SHAN Jian-feng
School of Electronic Information and Optical Engineering,Microelectronics,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
人脸表情识别; 深度学习; 稠密网络; 浅层网络; 特征融合
Keywords:
facial expression recognition; deep learning; DenseNet; shallow network; feature fusion
分类号:
TP391. 41
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 10. 025
摘要:
由于稠密网络(DenseNet) 模型具有独特的特征提取和传输方式,使其面对小数据集时在缓解网络过拟合的同时,可以取得不错的分类效果。 但是传统的 DenseNet 模型具有较深的网络结构,可能造成特征冗余和硬件内存的负担。 针对该问题,研究了一种相对浅层的稠密网络,通过压缩稠密网络的深度并增加每个模块中卷积核的数量来高效提取表情图像的隐性特征。 考虑到该稠密网络在提取特征时也舍弃了部分图像信息以及单一特征可能难以表达人脸表情图像的全部信息,利用 LDN( Local Directional Number Pattern,LDN) 算法提取表情图像的梯度方向纹理信息,与稠密网络提取的隐式特征进行特征融合,共同进入 Softmax 层进行表情分类。 该算法在 CK+和 Jaffe 数据集上进行仿真实验,获得了不错的识别率,在一定程度上证实了算法的有效性。
Abstract:
Because DenseNet model has a unique feature extraction and transmission mode,it can not only alleviate network overfitting but also achieve excellent classification effect when facing small data sets. However, the traditional DenseNet model has a deep network structure, which may cause feature redundancy and hardware memory burden. To solve this problem,we study a relatively shallow dense network and extract the recessive features efficiently by compressing the depth of the dense network and increasing the number of convolution kernels in each module. The local directional number pattern ( LDN) algorithm is used to extract gradient direction texture information of facial expression images, considering that the dense network abandoned part of image information and a single feature could not express all information of facial expression images. Feature fusion is carried out with implicit features extracted from dense network,and they are jointly entered into Softmax layer for facial expression classification. The algorithm is simulated on CK+ and Jaffe datasets,and high recognition rate is obtained,which proves the effectiveness of the algorithm to a certain extent.

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更新日期/Last Update: 2022-10-10