Evidential Fusion for Gender Profiling

Jianbing Ma, Weiru Liu, Paul Miller

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

4 Citations (Scopus)

Abstract

Gender profiling is a fundamental task that helps CCTV systems to
provide better service for intelligent surveillance. Since subjects being detected
by CCTVs are not always cooperative, a few profiling algorithms are proposed
to deal with situations when faces of subjects are not available, among which
the most common approach is to analyze subjects’ body shape information. In
addition, there are some drawbacks for normal profiling algorithms considered
in real applications. First, the profiling result is always uncertain. Second, for a
time-lasting gender profiling algorithm, the result is not stable. The degree of
certainty usually varies, sometimes even to the extent that a male is classified
as a female, and vice versa. These facets are studied in a recent paper [16] using
Dempster-Shafer theory. In particular, Denoeux’s cautious rule is applied for
fusion mass functions through time lines. However, this paper points out that if
severe mis-classification is happened at the beginning of the time line, the result
of applying Denoeux’s rule could be disastrous. To remedy this weakness,
in this paper, we propose two generalizations to the DS approach proposed in
[16] that incorporates time-window and time-attenuation, respectively, in applying
Denoeux’s rule along with time lines, for which the DS approach is a special
case. Experiments show that these two generalizations do provide better results
than their predecessor when mis-classifications happen.
Original languageEnglish
Title of host publicationInternational Conference on Scalable Uncertainty Management (SUM 2012)
PublisherAAAI Press
Pages514-524
Number of pages11
DOIs
Publication statusPublished - Sep 2012
EventInternational Conference on Scalable Uncertainty Management, SUM 2012 - , Germany
Duration: 19 Sep 2012 → …

Conference

ConferenceInternational Conference on Scalable Uncertainty Management, SUM 2012
CountryGermany
Period19/09/2012 → …

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  • Cite this

    Ma, J., Liu, W., & Miller, P. (2012). Evidential Fusion for Gender Profiling. In International Conference on Scalable Uncertainty Management (SUM 2012) (pp. 514-524). AAAI Press. https://doi.org/10.1007/978-3-642-33362-0_39