摘要
多目标遗传算法(MOGA)是求解多目标优化问题的有效工具,因而在求解实际问题中得到越来越广泛的应用。PCA是一种基于二阶统计的最小均方误差意义上的最优维数压缩技术,PCA方法所抽取特征的各分量之间是统计不相关的。在人脸识别的实际应用中,将多目标遗传算法引入到PCA所生成的特征空间的优化中,提出基于双重特征空间的人脸识别算法。通过对剑桥ORL库实验表明,该方法与传统的PCA相比,识别率得到明显提高。
Multi-objective genetic algorithm (MOGA) is an effective way to solve multi-objective optimization problem and is used more and more extensively in solution of practical problem. PCA (principal component analysis) is the optimal dimension compression technique based on second-order information, in the sense of mean-square error. Features extracted by PCA are statistically uncorrelated to each other. In the applications on face image recognition, optimization of characteristic space which is produced by PCA is introduced. A new face recognition method based on double characteristic space is proposed. The experiment to ORL face library shows that the new method provide satisfactory recognition performance.
出处
《计算机工程与设计》
CSCD
北大核心
2007年第1期112-114,共3页
Computer Engineering and Design
基金
湖南省自然科学基金项目(02JJY2091)
关键词
人脸识别
主成分分析
特征脸加权
特征空间优化
多目标遗传算法
face recognition
principal component analysis (PCA)
weighted eigenface (WE)
optimization of characteristic space
multi objective genetic algorithm (MOGA)