Fast Static Particle Swarm Optimization Based Feature Selection for Face Detection

Feature selection only using wrapper method in high-dimensional data space is always time-consuming. A new feature selection method, named fast static particle swarm optimization, is proposed for tackling this problem. It treats the whole initial feature set as a static particle swarm in which no new particle would be generated in high dimensional space, and the proposed method takes filter and wrapper strategy to pick out the most discriminative feature particle subset. Compared with the existing methods, experimental results show that the proposed method is faster than the existing methods in frontal face detection, and the detection error rate is lower than them on average.