An Evidential Improvement for Gender Profiling.

CCTV systems are broadly deployed in the present world. To ensure<br/>in-time reaction for intelligent surveillance, it is a fundamental task for real-world<br/>applications to determine the gender of people of interest. However, normal video<br/>algorithms for gender profiling (usually face profiling) have three drawbacks.<br/>First, the profiling result is always uncertain. Second, for a time-lasting gender<br/>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.