|
| 基于PCA与贝叶斯决策的人脸识别算法 |
| Research on face recognition methods based on principal component analysis and Bayesian decision |
| 投稿时间:2013-11-19 |
| DOI:10.3969/j.issn.1005-5630.2014.02.007 |
| 中文关键词: 主元分析 贝叶斯决策 人脸识别 |
| 英文关键词:principle component analysis Bayesian decision face recognition |
| 基金项目:黑龙江省科技厅自然科学基金项目(F201108) |
|
| 摘要点击次数: 4642 |
| 全文下载次数: 4396 |
| 中文摘要: |
| 研究了主元分析与贝叶斯决策相结合的人脸识别方法。利用主元分析提取人脸图像训练集的特征子空间,将训练图像和测试图像投影到该子空间,提取特征向量及计算统计特性,利用最小错误率贝叶斯决策规则对测试图像进行分类,从而实现人脸识别。大量实验表明:主元分析能将人脸图像的特征信息有效地映射在特征子空间,同时采用贝叶斯决策规则能够快速准确地对人脸图像进行分类。 |
| 英文摘要: |
| A method for face recognition based on principal component analysis(PCA)and Bayesian decision is described. Extracting feature subspace of face image training sets using PCA, the training images and test images are projected in the subspace, the eigenvectors are extracted and the statistical properties are calculated, adopt Bayesian decision based on minimum error rate to realize face recognition. Experimental results show that PCA is well in the subspace of face images to extract feature information, by Bayesian decision can quickly and accurately classify the face images. |
| HTML 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |