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Face recognition using common faces method

机译:使用普通人脸方法进行人脸识别

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摘要

In this paper, we propose a face recognition method called the commonface by using the common vector approach. A face image is regarded as a summation of a common vector which represents the invariant properties of the corresponding face class, and a difference vector which presents the specific properties of the corresponding face image such as face appearance, pose and expression. Thus, by deriving the common vector of each face class, the common feature of each person is obtained which removes the differences of face images belonging to the same person. For test face image, the remaining vector with each face class is derived with the similar procedure to the common vector, which is then compared with the common vector of each face class to predict the class label of query face by finding the minimum distance between the remaining vector and the common vector. Furthermore, we extend the common vector approach (CVP) to kernel CVP to improve the performance of CVP. The experimental results suggest that the proposed commonface approach provides a better representation of individual common feature and achieves lower error rates in face recognition. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:在本文中,我们提出了一种使用公共矢量方法的人脸识别方法,称为“公共人脸”。面部图像被视为代表相应面部类别的不变属性的公共向量的总和,而差异矢量则代表相应面部图像的特定属性(例如面部外观,姿势和表情)。因此,通过推导每个面部类别的公共矢量,获得了每个人的共同特征,该特征去除了属于同一个人的面部图像的差异。对于测试的人脸图像,使用与公共向量相似的过程来推导每个人脸类别的剩余向量,然后将其与每个人脸类别的共同向量进行比较,以通过找到两个人脸类别之间的最小距离来预测查询人脸的类别标签。剩余向量和公共向量。此外,我们将通用向量方法(CVP)扩展到内核CVP,以提高CVP的性能。实验结果表明,提出的常用人脸方法可以更好地表示单个常用特征,并在人脸识别中实现较低的错误率。 (c)2006模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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