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.

参考文献

［1］ SINGH J P, JAIN S, ARORA S, et al. Vision-based gait recognition: A survey［J］. IEEE Access, 2018,6:70497-70527.
［2］ RIDA I, ALMAADEED N, ALMAADEED S. Robust gait recognition: A comprehensive survey［J］. IET Biometrics, 2019,8(1):14-28.〖HJ1.08mm〗
［3］ WAN C S, WANG L, PHOHA V V. A survey on gait recognition［J］. ACM Computing Surveys, 2018,51(5). DOI: 10.1145/3230633.
［4］ KUMAR M, SINGH N, KUMAR R, et al. Gait recognition based on vision systems: A systematic survey［J］. Journal of Visual Communication and Image Representation, 2021,75. DOI: 10.1016/j.jvcir.2021.103052.
［5］王科俊,丁欣楠,邢向磊,等. 多视角步态识别综述［J］. 自动化学报, 2019,45(5): 841-852.
［6］ MAKIHARA Y, ADACHI D, XU C, et al. Gait recognition by deformable registration［C］// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2018. DOI: 10.1109/CVPRW.2018.00098.
［7］ CHOI S, KIM J, KIM W, et al. Skeleton-based gait recognition via robust frame-level matching［J］. IEEE Transactions on Information Forensics and Security, 2019,14(10):2577-2592.
［8］ LEE L, GRIMSON W E L. Gait analysis for recognition and classification［C］// Proceedings of the 5th IEEE International Conference on Automatic Face Gesture Recognition. 2002:155-162.
［9］ YOO J H, NIXON M S, HARRIS C J. Extracting gait signatures based on anatomical knowledge［C］// Proceedings of the 2002 BMVA Symposium on Advancing Biometric Technologies. 2002.
［10］SUN J D, WANG Y F, LI J, et al. View-invariant gait recognition based on kinect skeleton feature［J］. Multimedia Tools and Applications, 2018,77(19):24909-24935.
［11］BABAEE M, LI L W, RIGOLL G. Person identification from partial gait cycle using fully convolutional neural network［J］. Neurocomputing, 2019,338:116-125.
［12］HAN J, BHANU B. Individual recognition using gait energy image［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28(2):316-322.
［13］LIAO R J, CAO C S, GARCIA E B, et al. Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations［C］// Proceedings of the 2017 Chinese Conference on Biometric Recognition. 2017:474-483.
［14］WOLF T, BABAEE M, RIGOLL G. Multi-view gait recognition using 3D convolutional neural networks［C］// Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP). 2016:4165-4169.
［15］WU Z F, HUANG Y Z, WANG L, et al. A comprehensive study on cross-view gait based human identification with deep CNNs［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(2):209-226.
［16］CHAO H Q, HE Y W, ZHANG J P, et al. Gaitset: Regarding gait as a set for cross-view gait recognition［C］// Proceedings of the 2019 AAAI Conference on Artificial Intelligence. 2019,33(1):8126-8133.

［17］LIU W Y, WEN Y D, YU Z D, et al. SphereFace: Deep hypersphere embedding for face recognition［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:6738-6746.

［18］FU Y, WEI Y C, ZHOU Y Q, et al. Horizontal pyramid matching for person re-identification［C］// Proceedings of the 2019 AAAI Conference on Artificial Intelligence. 2019,33(1):8295-8302.
［19］ZHONG Z, ZHENG L, KANG G L, et al. Random erasing data augmentation［C］// Proceedings of the 2020 AAAI Conference on Artificial Intelligence. 2020,34(7):13001-13008.
［20］HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778.
［21］SCHROFF F, KALENICHENKO D, PHILBIN J. FaceNet: A unified embedding for face recognition and clustering［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015:815-823.
［22］WANG F, CHENG J, LIU W Y, et al. Additive margin softmax for face verification［J］. IEEE Signal Processing Letters, 2018,25(7):926-930.
［23］DUNNE R A, CAMPBELL N A. On the pairing of the softmax activation and cross-entropy penalty functions and the derivation of the softmax activation function［C］// Proceedings of the 8th Australian Conference on the Neural Networks. 1997:181-185.
［24］SUN Y F, CHENG C M, ZHANG Y H, et al. Circle loss: A unified perspective of pair similarity optimization［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020:6397-6406.
［25］WEI X S, WU J X, CUI Q. Deep learning for fine-grained image analysis: A survey［J］. arXiv preprint arXiv:1907.03069, 2019.
［26］YU S Q, TAN D L, TAN T N. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition［C］// Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06). 2006,4:441-444.
［27］WU X H, AN W Z, YU S Q, et al. Spatial-temporal graph attention network for video-based gait recognition［C］// Proceedings of the Asian Conference on Pattern Recognition. 2019:274-286.

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