ORCID Profile
0000-0002-6064-7290
Current Organisation
University of Leeds
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Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 06-2013
Publisher: Wiley
Date: 06-07-2015
Publisher: SPIE
Date: 19-02-2014
DOI: 10.1117/12.2038644
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: IEEE
Date: 04-2013
Publisher: IGI Global
Date: 2014
Abstract: Human face/gait-based gender recognition has been intensively studied in the previous literatures, yet most of them are based on the same database. Although nearly perfect gender recognition rates can be achieved in the same face/gait dataset, they assume a closed-world and neglect the problems caused by dataset bias. Real-world human gender recognition system should be dataset-independent, i.e., it can be trained on one face/gait dataset and tested on another. In this paper, the authors test several popular face/gait-based gender recognition algorithms in a cross-dataset manner. The recognition rates decrease significantly and some of them are only slightly better than random guess. These observations suggest that the generalization power of conventional algorithms is less satisfied, and highlight the need for further research on face/gait-based gender recognition for real-world applications.
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 09-2013
Publisher: Springer International Publishing
Date: 2014
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Xingjie Wei.