Gait based gender recognition using sparse spatio temporal features

Matthew Collins, Paul Miller, Jianguo Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


A gender balanced dataset of 101 pedestrians on a treadmill is presented. Gait is analysed for gender classification using a modification of a framework which has previously proven effective when used in behaviour recognition experiments. Sparse spatio temporal features from the video clips are classified using Support Vector Machines. Tuning parameters are investigated to find an effective feature descriptor for gender separation and an accuracy of 87% is achieved.
Original languageEnglish
Title of host publicationMultiMedia Modeling
Subtitle of host publication20th Anniversary International Conference, MMM 2014, Dublin, Ireland, January 6-10, 2014, Proceedings, Part II
EditorsCathal Gurrin, Frank Hopfgartner, Wolfgang Hurst, Håvard Johansen, Hyowon Lee, Noel O’Connor
Place of PublicationBerlin
Number of pages12
ISBN (Electronic)9783319041179
ISBN (Print)9783319041162
Publication statusPublished - 2014
Event20th Anniversary International Conference on MultiMedia Modeling - Conference Centre inside the Guinness Storehouse, Dublin, United Kingdom
Duration: 6 Jan 201410 Jan 2014

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743


Conference20th Anniversary International Conference on MultiMedia Modeling
Abbreviated titleMMM 2014
CountryUnited Kingdom
Internet address

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