Gait recognition using Gait Entropy Image

By Khalid Bashir, Tao A. Xiang, Shaogang Gong. International Conferences on Imaging for Crime Detection and Prevention, 2009

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The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition (2012, methodology): “In this paper, we deal with six such state-of-the-art gait features: GEI, Frequency-Domain Feature [4] (referred to as FDF in this paper), Gait Entropy Image (GEnI) [5], Masked GEI based on GEnI [7] (referred to as MGEI in this paper), Chrono-Gait Image (CGI) [6], and Gait Flow Image (GFI) [8].”
GEINet: View-invariant gait recognition using a convolutional neural network (2016, methodology): “, gait entropy image (GEnI) [1] and masked GEI based on GEnI [2]) to mitigate the effect of appearance change caused by clothing and carrying status.”
GaitPart: Temporal Part-Based Model for Gait Recognition (2020, methodology): “For the temporal changing characteristics, some of these state-of-art methods do not model temporal features explicitly, which leads to the loss of important invariant features in time series[23, 7, 3, 26].”
A data augmentation methodology for training machine/deep learning gait recognition algorithms (2016, methodology): “A slightly different approach by Bashir et al [12] took advantage of the ability of Information Entropy to calculate the average amount of information in a signal; they introduced the Gait Entropy Image (GEnI), which is obtained by calculating the Shannon Entropy for each pixel in a sequence of…”