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摘要: 如何描述每个个体人脸的特征,使之区别于其他个体,是人脸识别研究中的关键问题 之一.近年来提出了大量的方法,其中随着主元分析在人脸识别中的成功应用之后,子空间分析 因其具有描述性强、计算代价小、易实现及可分性好的特点,受到了广泛的关注.文中结合近年 来已发表的文献,按照线性和非线性的划分,对子空间分析在人脸识别中的应用作一回顾、比较 和总结,以供其他人参考.Abstract: For face recognition, how to extract discriminant features from face images is a key problem. Many methods have been proposed, and among these methods the subspace analysis has been given more and more attention owing to its good properties, since principal component analysis (PCA) was applied successfully. In this paper, all the subspace analysis methods which have been successfully applied to face recognition will be reviewed and some summaries will be given.
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Key words:
- Principal component analysis /
- subspace analysis /
- face recognition
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