摘要
本文以人脸识别为目标,重点分析基于子空间分析的人脸特征提取技术.首先介绍人脸识别系统的构成,其次分析人脸识别的关键技术,如人脸检测、特征提取和图像预处理等,重点分析人脸识别的各种算法,根据小波在对图像数据矩阵的处理的高效性,以及LDA训练样本维数少的缺陷,PCA不能利用数据的高阶统计特性,本文将这三种算法进行融合,并用MATLAB进行仿真实验,实验证明该方法的有效性.
When it comes to the face recognition, this paper exceptionally focuses on the facial feature extraction based on subspace analysis. Firstly, this paper introduces the constitution of the face recognition system, and then analyses the key technologies, such as the face detection, feature extraction, and image pretreatment processing. It mainly analyses the various face recognition algorithms. According to the high efficiency of wavelet in the processing of image data matrix and the shortcoming of less dimension of the LDA training sample, PCA cannot use higher order statistical properties of the data. Combining these three algorithms, this paper puts forward the improved recognition method. Simulation experiments with MATLAB are carried out and the results show the effectiveness of the method.
作者
江华丽
JIANG Hua-Li(Mirman Institute of Science and Technology, Fujian Normal University, Quanzhou 362332, Chin)
出处
《计算机系统应用》
2017年第2期151-157,共7页
Computer Systems & Applications
基金
福建省教育厅中青年项目(JAT160673)
福建省高校创新创业教育改革项目(SJZY-2015-02)
关键词
人脸识别
特征提取
融合算法
face recognition
feature extraction
fusion algorithm
