基于改进残差网络的联合损失步态特征识别

计算机与现代化 ›› 2022, Vol. 0 ›› Issue (04): 27-32.

基于改进残差网络的联合损失步态特征识别

(1.四川大学电子信息学院，四川成都610065;2.四川大学华西医院国家老年疾病临床医学研究中心，四川成都610065)

出版日期:2022-05-07 发布日期:2022-05-07
作者简介:贺璇(1997—)，女，河南三门峡人，硕士研究生，研究方向：计算机视觉，图像处理，E-mail: 631425077@qq.com; 刘怡欣(1980—)，男，四川攀枝花人，副主任医师，博士后在站，研究方向：老年心血管疾病防治，E-mail: liuyixin@wchscu.cn; 通信作者：何小海(1964—)，男，四川绵阳人，教授，博士生导师，博士，研究方向：图像处理，模式识别，图像通信，E-mail: nic5602@scu.edu.cn; 卿粼波(1982—)，男，四川成都人，副教授，博士生导师，博士，研究方向：多媒体通信与信息系统，人工智能与计算机视觉，嵌入式系统，E-mail: qing_lb@scu.edu.cn; 陈洪刚(1991—)，男，四川开江人，副研究员，博士研究生，研究方向：图像/视频处理，E-mail: honggang_chen@scu.edu.cn。
基金资助:
国家自然科学基金资助项目(61871278); 四川省科技计划项目(2021YFS0239); 成都市重大科技应用示范项目(2019-YF09-00120-SN)

Gait Feature Recognition Based on Improved Residual Network and Joint Loss Function

(1. College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China;

2. National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610065, China)

Online:2022-05-07 Published:2022-05-07

摘要/Abstract

摘要： 针对现有的步态识别模型识别准确率不够高、提取特征层次较浅等问题，在步态识别网络GaitSet的基础上，提出一种新的基于改进残差网络的联合损失步态特征识别模型Res-GaitSet。步态作为一种独特而有效的远距离识别生物特征，可以在老年医学评估、社会秩序保障等方面被广泛应用。新网络在特征提取模块中引入残差单元，并采用多个损失函数联合使用的方式，此方法可有效提高步态识别模型的准确性和鲁棒性。实验结果表明，改进后的网络Res-GaitSet在CASIA-B数据集的多个场景和不同识别角度下的准确率均有提升。同时，将改进后的网络用于自建步态数据集，对比于原网络，改进后的网络识别效果在不同角度下也均有提升，充分验证了改进模型的有效性。

关键词: 步态识别, 特征提取, 残差网络, 步态轮廓图, 联合损失函数

Abstract: Aiming at the problems of insufficient recognition accuracy and shallow feature extraction level of the existing gait recognition models, a new joint loss gait feature recognition model Res-GaitSet based on improved residual network is proposed on the basis of GaitSet network. As a unique and effective biometric for long-distance recognition, gait can be widely used in geriatric evaluation, social order security and so on. In the new network, residual elements are introduced into the feature extraction module, and multiple loss functions are used together. This method effectively improves the accuracy and robustness of gait recognition model. The experimental results show that the accuracy of the improved network Res-GaitSet is improved in multiple scenes and different recognition angles of CASIA-B dataset. At the same time, the improved network is used for self built gait data set. Compared with the original network, the recognition effect of the improved network is also improved from different angles, which fully verifies the effectiveness of the improved model.

Key words: gait recognition, feature extraction, residual network, gait contour map, joint loss function

贺璇, 刘怡欣, 何小海, 卿粼波, 陈洪刚. 基于改进残差网络的联合损失步态特征识别[J]. 计算机与现代化, 2022, 0(04): 27-32.

HE Xuan, LIU Yi-xin, HE Xiao-hai, QING Lin-bo, CHEN Hong-gang. Gait Feature Recognition Based on Improved Residual Network and Joint Loss Function[J]. Computer and Modernization, 2022, 0(04): 27-32.

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