A robust covariate‐invariant gait recognition based on pose features. Issue 6 (20th October 2022)

• Record Type: Journal Article
• Authors: Parashar, Anubha; Parashar, Apoorva; Shekhawat, Rajveer Singh
• Other Names: Rida Imad guestEditor.; Marcialis Gian Luca guestEditor.; Fei Lunke guestEditor.; Istrate Dan guestEditor.; Fierrez Julian guestEditor.
• Is Part Of: IET biometrics. Volume 11:Issue 6(2022)
• Page Start: 601
• Page End: 613
• Publication Date: 2022-10-20
• Subjects: biometrics -- covariates -- deep learning -- gait recognition -- pose estimation; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/bme2.12103
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract: Gait recognition uses video of human gait processed by computer vision methods to identify people based on walking style. The complexity introduced by covariates makes the previous methods less efficient and inaccurate. This study proposes an approach based on pose features to attempt gait recognition of people with an overcoat, carrying objects, or other covariates. It aims to estimate human locomotion using Convolutional Neural Networks. Gathering video data, extracting video frames in a particular order, posture estimation for each frame, using multilayer RNN for gait recognition from the pose, and obtaining one‐dimensional object vectors, are all critical steps. Furthermore, these one‐dimensional identification vectors are stored in a data set along with the name of the person walking in the video. The proposed data set is used to train a classification model to predict the person in a new video by first processing it to get its identification vector and then to use it as a test case in the classification model. A graphical user interface was also developed so that anyone with no programming or technical experience can easily use the tool. The developed application does everything for gait detection from mp4 videos by obtaining the identification vectors and saving them into the data set. Using this application, one can quickly identify the person walking in a video. The results obtained offered an accuracy from 60.88% to 95.23%.