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.
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Ma, J., Liu, W., Miller, P. (2012). Evidential Fusion for Gender Profiling. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_39
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DOI: https://doi.org/10.1007/978-3-642-33362-0_39
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