Based on ORL face database, recognizes correlation coefficients of principal component extracted by KPCA.
基于OR L人脸库，识别核主成分分析提取出的主成分的相关性系数。
Finally, it designs a simple face recognition system to prove the validity, and experiments on the ORL face database.
最后，对文中讨论的上述算法的有效性，设计了一个简单的人脸识别系统，用OR L人脸像数据库进行了实验验证。
The experiments on ORL face database show that feature weighted methods are effective and general to face recognition.
最后用标准人脸库orl进行了实验，实验结果表明特征加权方法对人脸识别是有效且通用的。
In ORL face database, the experimental results prove that the algorithm outperforms traditional methods in small sample size problem.
在OR L人脸库上的实验结果说明，该算法对小样本数据的识别具有明显优势。
It is proved that the new algorithm has the good ability to extract eye feature by experiments on the ORL face database with 400 face images.
在具有以上干扰的OR L人脸库的400幅图像上的实验证明，该算法具有较好的眼睛特征抽取能力。
The experimental results on ORL face database show that the method proposed has very good classification capability and high recognition rate.
在ORL标准人脸库及现实人脸图像上的实验结果表明该方法具有很好的鉴别分析能力。
The experiment using the ORL face database has acquired a recognition rate of over 99.3% to our knowledge, this is also the better result using this database up to now.
本文在OR L人脸数据库上实验获得了超过99.3%的识别率，这也是我们所知道的在这一数据库上迄今为止较好的识别结果。
The experiments on the ORL face database show that the recognition rate of the proposed method is high when pose, illumination condition, face expression and training sample number change.
在OR L人脸库的测试结果表明，在姿态、光照、表情、训练样本数目变化的情况下，该算法都具有较好的识别率。
The experiment results based on ORL and FERET face database show that the method is efficient and feasible.
基于ORL和FERET人脸数据库的实验结果表明提出的方法具有有效性和可行性。
The results of experiments at ORL face image database show that the proposed method is feasible and effective.
对OR L人脸图像库的实验结果表明所给方法是可行的、有效的。
In the paper, we use nearest neighbor classifier to carry the experiment through the ORL person face database.
本文实验选用最近邻分类器，并利用OR L人脸数据库进行对比实验。
The experimental results on Olivetti Research Laboratory (ORL) face database and YALE face database show that the new methods are better than original NMF in terms of recognition rate.
实验结果表明提出的两种特征提取方法在识别率方面整体上好于原非负矩阵分解特征提取（NMF）方法。
ORL face image database, a total of 40 per 10 images, each of which the first five as training samples, after the 5 categories as a test sample, correct classification rate statistics.
OR L人脸图像库，共40人，每人10幅图像，其中每人的前5幅作为训练样本，后5幅作为测试分类样本，统计正确分类率。
ORL face image database, a total of 40 per 10 images, each of which the first five as training samples, after the 5 categories as a test sample, correct classification rate statistics.
OR L人脸图像库，共40人，每人10幅图像，其中每人的前5幅作为训练样本，后5幅作为测试分类样本，统计正确分类率。
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