Adaptive Face Gender Recognition Based on NSGA-II and Compressive Sensing
Lin Zhang · Applied Mechanics and Materials · 2015
In the face gender recognition system, in addition to want to obtain high recognition rate, but also want to be able to achieve recognition quickly which relates to multi-objective optimization problem. In this paper, we proposed an adaptive face gender recognition method based on NSGA-II which can make recognition performance and running time optimal at the same time. In our method, we use CS to extract features and Extreme Learning Machine (ELM) to achieve features recognition. Then, NSGA-II is used to optimize the method. Experimental results show that the proposed method using NSGA-II solving optimization problems can obtain evenly distributed Pareto optimal solution set at a fast convergence in the solution space which demonstrates feasibility and effectiveness of using NSGA-II to solve multi-objective face gender recognition problems.