论文:2019,Vol:37,Issue(5):1070-1076
引用本文:
吴丹, 方明, 付飞蚺. 融合面部特征的Spindle Net行人重识别网络[J]. 西北工业大学学报
WU Dan, FANG Ming, FU Feiran. Person Re-Identification Net of Spindle Net Fusing Facial Feature[J]. Northwestern polytechnical university

融合面部特征的Spindle Net行人重识别网络
吴丹1, 方明1,2, 付飞蚺1
1. 长春理工大学 计算机科学技术学院, 吉林 长春 130022;
2. 长春理工大学 人工智能学院, 吉林 长春 130022
摘要:
目前在行人重识别(person re-identification)领域对行人特征的提取主要集中在整体行人或肢体躯干分别提取特征,较少使用面部特征。将面部特征融入到网络中以提高行人重识别的准确率。在行人重识别网络Spindle Net的框架中引入MTCNN面部提取网络,通过提高面部特征在整体行人特征中的权重来提高行人重识别的准确率。实验结果表明,文中提出的网络相比于Spindle Net在CUHK01,CUHK03,VIPeR,PRID,i-LIDS,3DPeS数据集上Rank-1的准确率平均提升7%。
关键词:    行人重识别    面部    卷积神经网络   
Person Re-Identification Net of Spindle Net Fusing Facial Feature
WU Dan1, FANG Ming1,2, FU Feiran1
1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China;
2. School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130022, China
Abstract:
In the field of person re-identification, the extraction of pedestrian features is mainly focused on the extraction of features from the whole pedestrian or limb torso, and the facial features are less used. The facial features is integrated into the network to enhance pedestrian recognition accuracy rate. By introducing the MTCNN facial extraction network in the framework of person re-identification network Spindle Net, and improves the accuracy of person re-identification by improving the weight of facial features in the overall pedestrian characteristics. The experimental results show that the accuracy of Rank-1 on the CUHK01, CUHK03, VIPeR, PRID, i-LIDS, and 3DPeS data sets is 7% higher than that of Spindle Net.
Key words:    person re-identification    facial    convolutional neural network   
收稿日期: 2018-10-08     修回日期:
DOI: 10.1051/jnwpu/20193751070
基金项目: 吉林省重点科技成果转化项目(20170307002GX)资助
通讯作者: 方明(1977-),长春理工大学副教授,主要从事鲁棒的图像处理、机器视觉技术研究。e-mail:fangming@cust.edu.cn     Email:fangming@cust.edu.cn
作者简介: 吴丹(1992-),女,长春理工大学硕士研究生,主要从事图像处理应用技术研究。
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