Improvement of a face detection system by evolutionary multi-objective optimization

This paper presents the application of evolutionary multi-objective optimization (EMO) to the improvement of a face detection system. The face detection system is based on the boosted cascade system, and analyzes image positions on different scales in a three-step-procedure. Based on threshold settings, the algorithm decides whether to continue with the test on a finer scale at the current position. Thus, the thresholds for all scales and stages have a major influence on the performance of the system, and become the subject of the evolutionary optimization according to three objectives: low false positive rate, high detection rate and low processing time. The used EMO is the extension of the standard genetic algorithm to the EMO case by using fuzzy Pareto dominance as a meta-heuristic. The application of this EMO to the face detection system is explored and discussed using images from a standard face detection benchmark dataset. From the runtime analysis of the EMO it can be concluded that the algorithm reliably approaches the Pareto set of the problem.