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Review on data uncertainty in face recognition using appearance-based methods

机译:使用基于外观的方法回顾人脸识别中的数据不确定性

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摘要

The face images should not be the completely accurate for representation and for an observation. To reducing the uncertainty for representation of the face images and to improving the accuracy of face recognition, more observation of the same person face images is required in the face recognition. In the real world face recognition system the uncertainty highly occurred because the limited number of available face images of subject and due to this there is high uncertainty is occurred. In this paper, develop the model which is to improve the accuracy in the face recognition by reducing the data uncertainty. The model is to reduce the uncertainty of face images representation by synthesizing the virtual training samples. Here, the useful training samples are selected, which are comparable to the test sample from the set of all the original training samples and synthesized virtual training sample.
机译:脸部图像对于表示和观察而言不应完全准确。为了减少面部图像的表示的不确定性并提高面部识别的准确性,在面部识别中需要更多地观察同一个人的面部图像。在现实世界中的面部识别系统中,由于对象的可用面部图像数量有限,因此不确定性很高,因此存在很大的不确定性。本文提出了一种通过减少数据不确定性来提高人脸识别精度的模型。该模型是通过合成虚拟训练样本来减少面部图像表示的不确定性。在这里,从所有原始训练样本和合成的虚拟训练样本中选择有用的训练样本,这些样本与测试样本相当。

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