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DOI: 10.14569/IJARAI.2012.010601
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Human Gait Gender Classification in Spatial and Temporal Reasoning

Author 1: Kohei Arai
Author 2: Rosa Andrie Asmara

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 1 Issue 6, 2012.

  • Abstract and Keywords
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Abstract: Biometrics technology already becomes one of many application needs for identification. Every organ in the human body might be used as an identification unit because they tend to be unique characteristics. Many researchers had their focus on human organ biometrics physical characteristics such as fingerprint, human face, palm print, eye iris, DNA, and even behavioral characteristics such as a way of talk, voice and gait walking. Human Gait as the recognition object is the famous biometrics system recently. One of the important advantage in this recognition compare to other is it does not require observed subject’s attention and assistance. This paper proposed Gender classification using Human Gait video data. There are many human gait datasets created within the last 10 years. Some databases that widely used are University of South Florida (USF) Gait Dataset, Chinese Academy of Sciences (CASIA) Gait Dataset, and Southampton University (SOTON) Gait Dataset. This paper classifies human gender in Spatial Temporal reasoning using CASIA Gait Database. Using Support Vector Machine as a Classifier, the classification result is 97.63% accuracy.

Keywords: Gait Gender Classification; Gait Energy Motion; CASIA Gait Dataset.

Kohei Arai and Rosa Andrie Asmara, “Human Gait Gender Classification in Spatial and Temporal Reasoning” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(6), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010601

@article{Arai2012,
title = {Human Gait Gender Classification in Spatial and Temporal Reasoning},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010601},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010601},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {6},
author = {Kohei Arai and Rosa Andrie Asmara}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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