@inproceedings{52b90efcc2a04668ba5bcd56f4301d82,
title = "Gait recognition using binarized statistical image features and histograms of oriented gradients",
abstract = "This paper presents a gait recognition method using the combination of motion history image (MHI), binarized statistical image features (BSIF) and histograms of oriented gradients (HOG). The method first encodes the motion pattern and direction of the gait cycle in motion history image. Subsequently, performing convolution on the motion history image using pre-learnt filters as kernel, binarized statistical image features are generated by summing the convolution output images. Histograms of oriented gradients are then computed on binarized statistical image features. Gait signature of a gait cycle is attained by accumulating all the HOG descriptors. Experimental result shows that the proposed method performs promisingly in gait recognition.",
keywords = "Binarized Statistical Image Features, Gait, Gait Recognition, Histograms of Oriented Gradients, Motion History Image",
author = "Mogan, {Jashila Nair} and Lee, {Chin Poo} and Lim, {Kian Ming} and Tan, {Alan W.C.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017 ; Conference date: 27-11-2017 Through 29-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICORAS.2017.8308067",
language = "English",
series = "Proceeding of 2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "Proceeding of 2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017",
address = "United States",
}