摘要： 针对传统人脸识别弹性图匹配算法空间复杂度高、实时性较差的问题，提出一种弹性图匹配改进算法，将人脸图片特征点经Gabor小波预处理后，结合主成分分析(PCA)和Fisher线性判别方法(FLD)对生成的特征矢量进行处理，降低维数，减少计算量，同时在不降低识别率的前提下，提高识别速度。与传统的PCA算法、FLD算法、EGM算法进行仿真比较，证明该改进算法识别率高、实时性好。

关键词: 人脸识别, 弹性图匹配, 特征提取, 主成分分析, Fisher线性判别

Abstract: This paper presents a new method for optimization of Elastic Graph Matching(EGM) algorithm in face recognition. After face image graph is composed of feature vector extracted from 2D Gabor wavelet, labeled graph vector is obtained. The proposed algorithm adopts the Principal Component Analysis(PCA) and FLD algorithm reduces dimensionality of labeled graph vector. In comparison with the conventional methods, the proposed approach can obtain satisfactory results in the perspectives of recognition rates and speeds.

Key words: face recognition, elastic graph matching, feature extraction, Principal Component Analysis(PCA), Fisher Linear Discriminant(FLD)

中图分类号: 

• TP391.4