An Evidential Improvement for Gender Profiling.

Jianbing Ma, Weiru Liu, Paul Miller

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

8 Citations (Scopus)

Abstract

CCTV systems are broadly deployed in the present world. To ensure
in-time reaction for intelligent surveillance, it is a fundamental task for real-world
applications to determine the gender of people of interest. However, normal video
algorithms for gender profiling (usually face profiling) have three drawbacks.
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. Third, for a robust profiling result in cases were a person’s face is not visible, other features, such as body shape, are required. These algorithms may provide different recognition results - at the very least, they will provide different degrees of certainties. To overcome these problems, in this paper, we introduce an evidential approach that makes use of profiling results from multiple algorithms over a period of time. Experiments show that this approach does provide better results than single profiling results and classic fusion results.
Original languageEnglish
Title of host publicationInternational Conference on Belief Functions (BELIEF'12):
PublisherSpringer-Verlag
Pages29-36
Number of pages8
DOIs
Publication statusPublished - May 2012
EventThe 2nd International Conference on Belief Functions - Compiegne, France
Duration: 01 May 201201 May 2012

Conference

ConferenceThe 2nd International Conference on Belief Functions
CountryFrance
CityCompiegne
Period01/05/201201/05/2012

Bibliographical note

Medium of Output: Printer proceedings

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  • Cite this

    Ma, J., Liu, W., & Miller, P. (2012). An Evidential Improvement for Gender Profiling. In International Conference on Belief Functions (BELIEF'12): (pp. 29-36). Springer-Verlag. https://doi.org/10.1007/978-3-642-29461-7_3