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

Abstract

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

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.

References ［27］

［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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