NDLI: Pose-based deep gait recognition

Content Provider	IET Digital Library
Author	Sokolova, Anna Konushin, Anton
Abstract	Human gait or walking manner is a biometric feature that allows identification of a person when other biometric features such as the face or iris are not visible. In this study, the authors present a new pose-based convolutional neural network model for gait recognition. Unlike many methods that consider the full-height silhouette of a moving person, they consider the motion of points in the areas around human joints. To extract motion information, they estimate the optical flow between consecutive frames. They propose a deep convolutional model that computes pose-based gait descriptors. They compare different network architectures and aggregation methods and experimentally assess various body parts to determine which are the most important for gait recognition. In addition, they investigate the generalisation ability of the developed algorithms by transferring them between datasets. The results of these experiments show that their approach outperforms state-of-the-art methods.
Starting Page	134
Ending Page	143
Page Count	10
ISSN	20474938
Volume Number	8
e-ISSN	20474946
Issue Number	Issue 2, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/8/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5046
Journal	IET Biometrics
Publisher Date	2018-09-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Aggregation Method Biometric Feature Biomimetics Computer Vision And Image Processing Technique Computes Pose-based Gait Descriptors Convolutional Neural Network Model Deep Convolutional Model Different Network Architectures Face Feature Extraction Full-height Silhouette Gait Analysis Human Gait Human Joints Image Motion Analysis Image Recognition Image Sequence Motion Information Moving Person Neural Nets Pose Estimation Pose-based Deep Gait Recognition Walking Manner
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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