Person Re-Identification Net of Spindle Net Fusing Facial Feature

融合面部特征的Spindle Net行人重识别网络

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

Received: 8 October 2018

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.

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%。