| 基于特征融合的三维人脸识别 |
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| 引用本文： | 常俊彦,达飞鹏,蔡亮.基于特征融合的三维人脸识别[J].东南大学学报(自然科学版),2011,41(1):47-51. |
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| 作者姓名： | 常俊彦 达飞鹏 蔡亮 |
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| 作者单位： | 东南大学自动化学院; |
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| 基金项目： | 国家自然科学基金资助项目(60775025); 新世纪优秀人才支持计划资助项目(NCET-07-0178) |
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| 摘 要： | 针对单一的人脸特征在识别中的局限性,将基于深度图像的全局特征和基于测地线的局部特征进行融合,以提高识别率.将三维人脸点云转换为深度图像后进行预处理,然后使用主成分分析法(PCA)找到一个低维的特征脸空间,依照最近邻法则将其与库集样本进行匹配,所得结果即为全局特征;将测试样本与模板人脸进行匹配,得到35个特征点,这些特征...
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| 关 键 词： | 深度图像 PCA 测地线距离 特征融合 |
3D face recognition based on feature fusion |
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| Chang Junyan,Da Feipeng,Cai Liang.3D face recognition based on feature fusion[J].Journal of Southeast University(Natural Science Edition),2011,41(1):47-51. |
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| Authors: | Chang Junyan Da Feipeng Cai Liang |
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| Institution: | Chang Junyan Da Feipeng Cai Liang(School of Automation,Southeast University,Nanjing 210096,China) |
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| Abstract: | In order to overcome the limitation of sole facial feature in recognition,a novel approach is presented to improve the accuracy by fusing the global feature based on the range image and the local facial feature based on the geodesic curve.The global feature is extracted using the following steps.First,cloud data is converted to range image.After preprocessing and normalized steps are applied,the principal component analysis(PCA) is used to find a low dimensional feature face space and achieve match score in... |
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| Keywords: | range image principal component analysis(PCA) geodesic distance feature fusion |
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