摘要
鉴于Gabor特征对光照、表情等变化比较鲁棒,并已在人脸识别领域取得成功应用,提出了一种改进的Gabor-LDA算法。首先对人脸图像进行多方向、多尺度Gabor小波滤波,然后对得到的特征向量使用改进的主成分分析方法(PCA)变换降维,采用自适应加权原理重建类内散布矩阵和类间散布矩阵,从而改进了最佳鉴别分析(LDA)判别函数,有效地解决了训练样本类均值与类中心的偏离问题。对Yale人脸库的数值试验表明,该算法比传统算法有更好的性能。
Since Gabor feature is robust to illumination and expression variations and has been successfully used in face recognition. A novelty method of face recognition based on Gabor-LDA is presented. First, the proposed method decomposes the face image by convolving the face image with multi-orientation and multi-scale Gabor filters to extract the eigenvectors. And then an improved algorithm of principal component analysis （PCA） is proposed. This algorithm is used to decrease the dimension of the eigenvector. The method of adaptively weight is used to reconstruct the within-class scatter matrix and between-class scatter, and then the linear discriminant analysis （LDA） function is improved. The problem of the class mean of training samples deviates from the center of this class is resolved by this improved LDA discriminate function. The numerical experiments on facial database of YALE show this method achieves better performance of face recognition than traditional methods.
出处
《计算机工程与设计》
CSCD
北大核心
2009年第14期3396-3398,3405,共4页
Computer Engineering and Design
基金
重庆市自然科学基金项目(CSTC
2006BB2365)
重庆市教委科学技术研究基金项目(KJ060504)
关键词
模式识别
GABOR小波变换
主成分分析
最佳鉴别分析
人脸识别
pattern recognition
Gabor wavelet transform
principle component analysis （PCA）
linear discriminant analysis （LDA）
face recognition
