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Gait-Based Gender Classification Using Kinect Sensor

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Curricular Issues in Computing and Information Technology Programs I

Tagged Division

Computing & Information Technology

Tagged Topic

Diversity

Page Count

11

Page Numbers

26.808.1 - 26.808.11

DOI

10.18260/p.24145

Permanent URL

https://peer.asee.org/24145

Download Count

292

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Paper Authors

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Mohammed Eltaher Dept. of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT

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Mohammed is a full-time Ph.D. student of Computer Science and Engineering at the University of Bridgeport. He received his B.S degree in Computer Science from Sebha University, Libya in 2000 and the M.S. degree in Intelligent System from University Utara Malaysia in 2005. He worked as assistant lecturer at Department of Computer Science, Sebha University from November 2005 to January 2008. Mohammed has research interests in the areas of data mining, information retrieval and multimedia database.

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Yawei Yang University of Bridgeport

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Yawei is a full-time Master student of Computer Science and Engineering at the University of Bridgeport. He received his B.S degree in Software Engineering from Tianjin University of Technology, China in 2012. He worked as assistant at Department of Computer Science, University of Bridgeport from August 2013 to December 2014.

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Jeongkyu Lee University of Bridgeport

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Dr. Jeongkyu Lee is currently associate professor in Department of Computer Science and Engineering at University of Bridgeport. He received Ph.D. in Computer Science from the University of Texas at Arlington in 2006. Before he pursued his doctorate, he worked as a database administrator for seven years with companies including Hana Bank and IBM Korea. His primary research area is in the multimedia database management system and analysis. Research interests include graph-based multimedia data modeling, indexing structure, big data system/analysis, social media data mining, and user profiling.

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Abstract

Gait-Based Gender Classification Using Kinect Sensor Abstract. Gender classification plays an important role in many applications such assurveillance systems and medical applications. Most of approaches for genderclassification are based on features of human face, voice and gait. Among theseapproaches, gait-based approaches are becoming more and more popular since the waythat they collect human gait information is non-contact and non-invasive. In this paper,we propose a novel method to classify human gender using their gaits. For the gait-basegender classification, we collect silhouettes of human walking pattern from MicrosoftKinect sensor, and extract two main gait features, i.e., Gait Energy Image (GEI) andDenoised Energy Image (DEI) from a sequence of an entire cycle of walking silhouetteimages. GEI is an appearance-based gait representation while DEI is used to removethe noises from GEI. For the gait features, we use a low dimensional feature vector torepresent the gait features. The extracted feature dataset are divided into two parts, i.e.,training and testing datasets. The training data set are used for training a SupportVector Machine (SVM) classifier while the testing dataset are used for the evaluation.Figure 1 shows overall procedure of proposed gait-based gender classification systemusing Microsoft Kinect sensor. Despite of the limitation of the dataset, i.e., differentraces and thickness of clothes which weaken the distinct differences between males andfemales, the average accuracy of the proposed approach reaches up to 87% under 10-folds validation. According to the experimental results, we know that GEI is anapplicable feature for human gait representation.Figure 1 General design of the system.

Eltaher, M., & Yang, Y., & Lee, J. (2015, June), Gait-Based Gender Classification Using Kinect Sensor Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24145

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