摘要
研究热红外成像人脸识别技术,提出一种基于主成分分析(PCA)和线性鉴别分析(LDA)的热红外成像人脸识别方法.针对热红外人脸图像的特点,首先对图像进行预处理得到一组标准热红外人脸图像,利用PCA算法对图像向量进行降维并提取其全局特征,对降维后的热红外人脸全局特征采用LDA算法训练生成一个使类间离散度最大、类内离散度最小的最佳分类器.最后,进行基于PCA+LDA的热红外人脸图像识别研究,实验结果表明该方法可获得较高的识别率.
A method for infrared face recognition is proposed based on principal component analysis (PCA) and linear discriminant analysis (LDA). According to the characteristics of infrared face images, a set of normalized infrared face images is gotten by preprocessing. The dimensionality of the image vector is reduced and the global features are extracted. The global features are used to generate a classifier which can minimize the within-class scatter and maximize the between-class scatter. Finally, an infrared face LDA is performed and the results recognition experiment based on the combination of PCA and show the high performance of the proposed method.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第2期160-164,共5页
Pattern Recognition and Artificial Intelligence
关键词
热红外成像
主成分分析(PCA)
线性鉴别分析(LDA)
人脸识别
直方图均衡化
Thermal Infrared Imaging, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Face Recognition, Histogram Equalization