Abstract:To perform the classification task with reduced complexity and acceptable performance by excluding irrelevant, redundant, or noisy from the problem representation, feature selection method based on multi-objective evolutionary wrappers is proposed. Firstly, features chosen by chromosome are used to parameterize face image again so as to get feature sets of active shape model. Then, multi-objective genetic algorithm is used to select features. Finally, proposed comprehensive fitness function and K nearest neighbor classifier are used to finish recognition. The efficiency of proposed method has been verified by experiments on Essex face database. Experimental results show that proposed method has improved the classification performance as well as reducing the representation dimensionality comparing with several advanced approaches.